{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5d4ce4b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from orion.data import load_signal"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec5c7e8c",
   "metadata": {},
   "source": [
    "# 1. Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "da695e98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1222819200</td>\n",
       "      <td>-0.366359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1222840800</td>\n",
       "      <td>-0.394108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1222862400</td>\n",
       "      <td>0.403625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1222884000</td>\n",
       "      <td>-0.362759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1222905600</td>\n",
       "      <td>-0.370746</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    timestamp     value\n",
       "0  1222819200 -0.366359\n",
       "1  1222840800 -0.394108\n",
       "2  1222862400  0.403625\n",
       "3  1222884000 -0.362759\n",
       "4  1222905600 -0.370746"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "signal_name = 'S-1'\n",
    "\n",
    "data = load_signal(signal_name)\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96767bae",
   "metadata": {},
   "source": [
    "# 2. Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fa524c4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.\n"
     ]
    }
   ],
   "source": [
    "from mlblocks import MLPipeline\n",
    "\n",
    "pipeline_name = 'aer'\n",
    "\n",
    "pipeline = MLPipeline(pipeline_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5b889d9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.\n"
     ]
    }
   ],
   "source": [
    "hyperparameters = {\n",
    "    'orion.primitives.aer.AER#1': {\n",
    "        'epochs': 5,\n",
    "        'verbose': True\n",
    "    }\n",
    "}\n",
    "\n",
    "pipeline.set_hyperparameters(hyperparameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f6eddcc",
   "metadata": {},
   "source": [
    "## step by step execution\n",
    "\n",
    "MLPipelines are compose of a squence of primitives, these primitives apply tranformation and calculation operations to the data and updates the variables within the pipeline. To view the primitives used by the pipeline, we access its `primtivies` attribute. \n",
    "\n",
    "The `lstm_dynamic_threshold` contains 7 primitives. we will observe how the `context` (which are the variables held within the pipeline) are updated after the execution of each primitive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ea1faf86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['mlstars.custom.timeseries_preprocessing.time_segments_aggregate',\n",
       " 'sklearn.impute.SimpleImputer',\n",
       " 'sklearn.preprocessing.MinMaxScaler',\n",
       " 'mlstars.custom.timeseries_preprocessing.rolling_window_sequences',\n",
       " 'orion.primitives.timeseries_preprocessing.slice_array_by_dims',\n",
       " 'orion.primitives.aer.AER',\n",
       " 'orion.primitives.aer.score_anomalies',\n",
       " 'orion.primitives.timeseries_anomalies.find_anomalies']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.primitives"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "554f5daa",
   "metadata": {},
   "source": [
    "### time segments aggregate\n",
    "this primitive creates an equi-spaced time series by aggregating values over fixed specified interval.\n",
    "\n",
    "* **input**: `X` which is an n-dimensional sequence of values.\n",
    "* **output**:\n",
    "    - `X` sequence of aggregated values, one column for each aggregation method.\n",
    "    - `index` sequence of index values (first index of each aggregated segment)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3b2c89f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['X', 'index'])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "context = pipeline.fit(data, output_=0)\n",
    "context.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e69c4925",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "entry at 1222819200 has value [-0.36635895]\n",
      "entry at 1222840800 has value [-0.39410778]\n",
      "entry at 1222862400 has value [0.4036246]\n",
      "entry at 1222884000 has value [-0.36275906]\n",
      "entry at 1222905600 has value [-0.37074649]\n"
     ]
    }
   ],
   "source": [
    "for i, x in list(zip(context['index'], context['X']))[:5]:\n",
    "    print(\"entry at {} has value {}\".format(i, x))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00be9392",
   "metadata": {},
   "source": [
    "### SimpleImputer\n",
    "this primitive is an imputation transformer for filling missing values.\n",
    "* **input**: `X` which is an n-dimensional sequence of values.\n",
    "* **output**: `X` which is a transformed version of X."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9658b615",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/orion/lib/python3.10/site-packages/sklearn/impute/_base.py:356: FutureWarning: The 'verbose' parameter was deprecated in version 1.1 and will be removed in 1.3. A warning will always be raised upon the removal of empty columns in the future version.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "dict_keys(['index', 'X'])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "step = 1\n",
    "\n",
    "context = pipeline.fit(**context, output_=step, start_=step)\n",
    "context.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86a369ed",
   "metadata": {},
   "source": [
    "### MinMaxScaler\n",
    "this primitive transforms features by scaling each feature to a given range.\n",
    "* **input**: `X` the data used to compute the per-feature minimum and maximum used for later scaling along the features axis.\n",
    "* **output**: `X` which is a transformed version of X."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d130e641",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['index', 'X'])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "step = 2\n",
    "\n",
    "context = pipeline.fit(**context, output_=step, start_=step)\n",
    "context.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5d15a6e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "entry at 1222819200 has value [-0.36635895]\n",
      "entry at 1222840800 has value [-0.39410778]\n",
      "entry at 1222862400 has value [0.4036246]\n",
      "entry at 1222884000 has value [-0.36275906]\n",
      "entry at 1222905600 has value [-0.37074649]\n"
     ]
    }
   ],
   "source": [
    "# after scaling the data between [-1, 1]\n",
    "# in this example, no change is observed\n",
    "# since the data was pre-handedly scaled\n",
    "\n",
    "for i, x in list(zip(context['index'], context['X']))[:5]:\n",
    "    print(\"entry at {} has value {}\".format(i, x))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b9eeac4",
   "metadata": {},
   "source": [
    "### rolling window sequence\n",
    "this primitive generates many sub-sequences of the original sequence. it uses a rolling window approach to create the sub-sequences out of time series data.\n",
    "\n",
    "* **input**: \n",
    "    - `X` n-dimensional sequence to iterate over.\n",
    "    - `index` array containing the index values of X.\n",
    "* **output**:\n",
    "    - `X` input sequences.\n",
    "    - `y` target sequences.\n",
    "    - `index` first index value of each input sequence.\n",
    "    - `target_index` first index value of each target sequence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "32759182",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['index', 'X', 'y', 'X_index', 'y_index'])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "step = 3\n",
    "\n",
    "context = pipeline.fit(**context, output_=step, start_=step)\n",
    "context.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2500967d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X shape = (10049, 100, 1)\n",
      "y shape = (10049, 1)\n",
      "X index shape = (10149,)\n",
      "y index shape = (10049,)\n"
     ]
    }
   ],
   "source": [
    "# after slicing X into multiple sub-sequences\n",
    "# we obtain a 3 dimensional matrix X where\n",
    "# the shape indicates (# slices, window size, 1)\n",
    "# and similarly y is (# slices, target size)\n",
    "\n",
    "print(\"X shape = {}\\ny shape = {}\\nX index shape = {}\\ny index shape = {}\".format(\n",
    "    context['X'].shape, context['y'].shape, context['index'].shape, context['y_index'].shape))\n",
    "\n",
    "y_ = context['y'].copy() # make a copy to copare later"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38070b3d-2276-4730-897a-f7178f17e05b",
   "metadata": {},
   "source": [
    "### slice array by dims\n",
    "\n",
    "this primitive selects a particular channel from `X` to try and reconstruct / predict it.\n",
    "\n",
    "* **input**:\n",
    "    * `X` n-dimensional array containing the input sequences.\n",
    "    * `target_index` which index from `X` to slice out.\n",
    "* **output**:\n",
    "    * `y` 1-dimenstional array containing the target sequences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "34eddaa6-02b6-48bf-b72d-512a51e597b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['index', 'X_index', 'y_index', 'X', 'y'])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "step = 4\n",
    "\n",
    "context = pipeline.fit(**context, output_=step, start_=step)\n",
    "context.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92a03719",
   "metadata": {},
   "source": [
    "### AER\n",
    "this is a hybrid prediction and reconstruction model using LSTM layers. you can read more about it in the [related paper](https://arxiv.org/pdf/2212.13558).\n",
    "\n",
    "* **input**: \n",
    "    - `X` n-dimensional array containing the input sequences for the model.\n",
    "    - `y` n-dimensional array containing the target sequences for the model.\n",
    "* **output**: `y_hat` predicted values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "062598c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "126/126 [==============================] - 7s 32ms/step - loss: 0.1940 - tf.__operators__.getitem_loss: 0.1879 - tf.__operators__.getitem_1_loss: 0.1999 - tf.__operators__.getitem_2_loss: 0.1881 - val_loss: 0.2274 - val_tf.__operators__.getitem_loss: 0.2168 - val_tf.__operators__.getitem_1_loss: 0.2292 - val_tf.__operators__.getitem_2_loss: 0.2343\n",
      "Epoch 2/5\n",
      "126/126 [==============================] - 3s 27ms/step - loss: 0.1871 - tf.__operators__.getitem_loss: 0.1794 - tf.__operators__.getitem_1_loss: 0.1948 - tf.__operators__.getitem_2_loss: 0.1795 - val_loss: 0.2227 - val_tf.__operators__.getitem_loss: 0.2166 - val_tf.__operators__.getitem_1_loss: 0.2294 - val_tf.__operators__.getitem_2_loss: 0.2154\n",
      "Epoch 3/5\n",
      "126/126 [==============================] - 3s 27ms/step - loss: 0.1814 - tf.__operators__.getitem_loss: 0.1740 - tf.__operators__.getitem_1_loss: 0.1886 - tf.__operators__.getitem_2_loss: 0.1745 - val_loss: 0.2135 - val_tf.__operators__.getitem_loss: 0.2092 - val_tf.__operators__.getitem_1_loss: 0.2056 - val_tf.__operators__.getitem_2_loss: 0.2337\n",
      "Epoch 4/5\n",
      "126/126 [==============================] - 3s 27ms/step - loss: 0.1788 - tf.__operators__.getitem_loss: 0.1700 - tf.__operators__.getitem_1_loss: 0.1876 - tf.__operators__.getitem_2_loss: 0.1701 - val_loss: 0.2133 - val_tf.__operators__.getitem_loss: 0.2134 - val_tf.__operators__.getitem_1_loss: 0.2015 - val_tf.__operators__.getitem_2_loss: 0.2369\n",
      "Epoch 5/5\n",
      "126/126 [==============================] - 3s 28ms/step - loss: 0.1761 - tf.__operators__.getitem_loss: 0.1656 - tf.__operators__.getitem_1_loss: 0.1866 - tf.__operators__.getitem_2_loss: 0.1657 - val_loss: 0.2134 - val_tf.__operators__.getitem_loss: 0.2132 - val_tf.__operators__.getitem_1_loss: 0.2061 - val_tf.__operators__.getitem_2_loss: 0.2283\n",
      "315/315 [==============================] - 1s 2ms/step\n",
      "315/315 [==============================] - 1s 3ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "dict_keys(['index', 'X_index', 'y_index', 'X', 'y', 'ry_hat', 'y_hat', 'fy_hat'])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "step = 5\n",
    "\n",
    "context = pipeline.fit(**context, output_=step, start_=step)\n",
    "context.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a9007107",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "entry at 1224979200 has value [-0.33798499], predicted value [-0.05156218]\n",
      "entry at 1225000800 has value [1.], predicted value [0.18158261]\n",
      "entry at 1225022400 has value [-0.34946279], predicted value [-0.07518177]\n",
      "entry at 1225044000 has value [-0.37232052], predicted value [0.17242043]\n",
      "entry at 1225065600 has value [1.], predicted value [0.12607291]\n"
     ]
    }
   ],
   "source": [
    "ry, fy = context['y'][:, 0], context['y'][:, -1]\n",
    "\n",
    "for i, y, y_hat in list(zip(context['y_index'], fy, context['fy_hat']))[:5]:\n",
    "    print(\"entry at {} has value {}, predicted value {}\".format(i, y, y_hat))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adeadad7",
   "metadata": {},
   "source": [
    "### score anomalies\n",
    "\n",
    "this primitive computes an array of errors comparing predictions and expected output.\n",
    "\n",
    "* **input**: \n",
    "    - `y` ground truth.\n",
    "    - `y_hat` reconstructed values.\n",
    "    - `fy_hat` forward prediction values.\n",
    "    - `ry_hat` reverse prediction values.\n",
    "* **output**: `errors` array of errors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "56173e5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['index', 'X_index', 'y_index', 'ry_hat', 'y_hat', 'fy_hat', 'X', 'y', 'errors'])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "step = 6\n",
    "\n",
    "context = pipeline.fit(**context, output_=step, start_=step)\n",
    "context.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "125b1fc9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "entry at 1224979200 has error value 1.000\n",
      "entry at 1225000800 has error value 1.351\n",
      "entry at 1225022400 has error value 1.351\n",
      "entry at 1225044000 has error value 1.351\n",
      "entry at 1225065600 has error value 1.351\n"
     ]
    }
   ],
   "source": [
    "for i, e in list(zip(context['y_index'], context['errors']))[:5]:\n",
    "    print(\"entry at {} has error value {:.3f}\".format(i, e))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "027a44a6-9c22-48d9-a1f2-26910f69cd08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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j3+a/E1uflH2850nq+vjHTEk6/Z0zOWn2t09qH9OfQOvinyf+dsHScSvrA/0G5u8jud9NsM+ZnO8usd8oKfnvVn63pJ7b3/6B1ic1/yX1twz2WwVa7y+dqXW9BPq8Sf3ek/N9BfocKfmciX2mlF57Sf3MiX22xPYJ9plS6/54K/eVpObV5Oyfku82NdKckmvqVtbf6m+XkvXBfu/4UuvenJI8k9z1Sc0HKc0bie0T7PtIrbQndpzU+Jz+1puPE2x9sHQkl788Gf9cgb7nlHzW+MuCfZZb+S4Crb8dv2mwzxls/9T4nGn9PSX3e3C7dXRAVVKWV9NDaqQzQ40xj46OJjo62vh3ZGRkOqYm6XRdp2T/eT7LPn2uMu/8vg2AYc9V4e3ft/qsf7Z6YWZsOgHA9C71aDl6lc/60W1q0uXnDQnOpSrg1uHtx8ox7K89AdP0Sv0STFh5OMHy8BALkVFOv/s0qZCPf3ad9Vk2qMW9DJi1HYChz1am34xtPus/eKIiH8/ZCcDUTnVpNXa1z/qFfRryyBdLAZjZrT7PfLcSq6bgcHku3Mkd69D6hzUADH++Cn1/8/2eiuQM5filmz7L2tQtxs+rjwb87MFUKBjOrlOR9G5yD1/+sy/B+hCrSpTDTfZQK1duOgBY0e8hGgz9F4DHKxdg3rbTdLy/JD8sPwTg83efJmX54p+9PFw+H4t2e77PL16sSp9ftgDQpEJ+/tl1xuecNk0lxuWmdolcZLFrLN5zjnqlcrPq4AWf7Xo+fA9fLfKk+ZnqhZm56QTP1ijMjI0nEnwOf3ngh7a16PjTegCeqFKQOVtP+az/q/cDPPblMgBql8zF2kMXubdwONtPRPLAPXk4cPYaJ69E+c1/fZuWY/gCz7KvW1Xn9ambfNabf+saxXKw8ehln/W1S+Ri7eGLALzaoCQ/rjjk97sKpGXNIkzfcNxn2Ut1ijFljW9eGfJsZfrH5uPG5fKyZM85n/XmfFy1aA62HLtM18alGbXkAOD/OjCn3ZtnS+bJwqHz1322yxlm5abDRZTDTa3iOVl/5JLP+rqlcrH6oOc4K/s9RP3YPFexYDg7T0X6pP3XzvV4YYzvfeN/9xVl2rpjAb+jQPeNB8vlZfGec7RvUILxKw4DvvewbCEWrsa7b5ivwwfuycOyfeeNdRZVoW29Evy44hDt6hVn4qojPvu2ql2MqWs9+77WsBTfLz3os978u03pVIeXxnryTe4sNi5cj+Gbl6rTY4onf/3RvQFPf7vCZ//+zcozZP5uAJa/8yD3f7rYZ/2IllV5c7rnepzb836af7XcZ32g+2dinqxaiD+3nASgVN4sHDx3ndFtatDl541AXJ42M18nY16uSedJGyiUPYSTV6IA+OW1urz4vScvftWqOj2nbvK5h9cpmYs1hy7St2k5xi47yOUbDt55rDyf/uX57AWzh3DqShRl8mVl/9lrAD75Mk9WG+evxVC+QDY0VWHHyUjj/hVIufzZ2HPmKgBFc4Vy7KLv/bnnQ2X46t/9Pss+a1mVt2K/b/PzrlXtYqw9dIGJr9Y2fiPvd29+RvZ4sAzfLPYcM35eA//XuZdNUylXIBvbTlwhR5iV8gWyGddYUr1Yqyi/rPe9roLlEfP90B/vvdz8ey57+0EeGLY44D4AS/s+SMPhnm2K5Qrj6MUbPuvnvH4/T3ztm5+fr1mE32LvjeZnupf5+vaXT733pSpFslMqTxZmbT7pN23m36Zzo1KM+c/3uv6kxb28H3tu73WdxaZxPcYFwGOVCvDXjtM+94TiucM4cuEGOcKshFk1Tl6JMq4vwMjvigLeuNB8nlJ5snAw3n3Y/Czq/mBpvl18IOB3+HLd4kxa7Xv/ev2hMnwdm8e95SZzvjffR73pNzPfO73XkPeYmuop2Lvcut8ygNnYtrXoFPs8914j3mcFwOcvVOWNXz3Xnbe8U61oDjYfuwzAuveakDebPeDx47twLZqan/yT5O0BCucI5cTlm5TKk4Xz16KJjHIa31XOMCs2i8qZyGjuyZeVfbH3KO99zfxcNX+n7zevwCdzdwX8Lh4qn49/d5/lqaqFmB17T/67T0MejS2Lep8jZubnqfc7MpcRJ7S/j1fGr/PZx3z/Mz+buzUuzXdLDlC9WA42xZZzcoZZuXTDYZSnAPJms3PuajT1S+dm5QHP72yOF957vAKD5+3yKQePa1eLDhM9n9N7D2p2bwHmbz8NwLTX6vK/733L4ebre17PB3j8K08Zz3u9me8Po1rXoOtkzzOrX7PyDJ2/m9J5s3Ag9nrzPocA7i+Th+X7z9Ph/pKMiy3/+nse58lq5/w1T2znLfd4y9kAZfNnZe+Za5TMk4XDF66j6/h8JyP/V42nqxUms1D0QNVfqX0iRQk6xnzgwIF89NFHCZZfuXIlQ7eYRzlclB/wV3onQ6Qhc5AuhBBCpAdvYViIW9WgTG4md6yb5O2HzNvFmHiVqEKktWdrFObzF6qldzISFRkZSfbs2VMlXs1Qr0vr378/V65cMf47dixwy48Qt5PT5U7vJAghhMjknG55FonUkdys5HLflnY8IXxlsmyXobqy2+127Pakd6sR4na5U8a3CCGEEEKkNikGiXSRyfJdhmoxF0IIIYQQ/t2ewYdCJCQNFEKkvTRtMb927Rr798dNAnPo0CE2b95Mrly5KFasWFqe+raSe9XdT35iIYQQ6U3iciFEZqJkshJ4mgbm69ev58EHHzT+/cYbbwDQrl07JkyYkJanFkIIIYS4q9ym+XqFSCBzhUdCpI80DcwbN26cKR4imeAjCnkiCSGESGdS3hDpRspBIh1ktl7JMsZcCCGEEOIOIHG5EELcvSQwF0IIIYS4A7ilyVykk8w21ldkDJkt10lgLkRSSFlICCFEOpO4XKSXzNalWIj0IIG5EEIIIcQdQOJykV4kLhfpIbNVCElgngqkBvvuJz+xEEKI9JYZJtQVQojMSgJzIYQQQog7gIwxF+kls7Vciowhs81tIIF5KtClPVUIIYQQaUzicpFeMluAJER6kMBciCSQ7oNCCCHSmzyKxG1x4yLcvJTeqRAi0/XUsKR3Au4G8qAUQgghRFqTruwizTljYFhJz98DLoDmCRUyW4AkRHqQFnMhhBBCiDuAxOUitQQMtK+fi/s7OjJu+7RNjhB+ZbYKIQnMU4E8J+9+8hsLIYRIb9JiLlJLwKzkio77O+Z63N+ZLUISIh1IYC6EEEIIcQeQsFykOXMw7opJv3QIAWS2vhoSmKcCmRjs7ic/sRBCiPQmLeYizZkDc2dc63nmCo+ESB8SmAshhBBC3AHs7puEEZXeyUg3Sf3sAyyTmGN7FzvS4pts0dfi/jZ1a5ee7CI9ZLZ8J4G5yNBsOPjA8hP3q9vSNR2K4zo/WIfznLo0XdMRiAUnebiS3skQGYiGi3csU2mkbknvpAghUoGGi/nX/8fOkFex4Lxt58woWmpL2BnyKi9rfwfdtoNlPveqh3lUXZ/2CUtl4VzjM+to6qk70vQ8qw5e4PKN2IoLZwxs+hkuH4UYU2DuTNuKjQbqNlbZe9BY3RR029xcoaayJ03Tk3I6T6krKamcSu+E3EF0chIZfLNMRgLzVCAdy4LRSem39Ir2F69a/uJn25DUTVIydbbMoYm2iRG20UG3rars5yfrEMopR29Dyjxm2wawPqQrpZUTQbd9SVtEG21hso6fg6vkSqcbaDZuZOoWopR6VltGV8ufTLR9elvPm4Wbt6kwr6PiBkDBfce2jJVRjvOAujW9kyHuAOHEdTHObvo7rbxp+ZVt9o5Jeq7cDsOt3wMwyDohyfu47sBi7ruWKTyvLWWqbXCan+u7JQc8f2z+Gf7oDt/cF2+Mubkre+o3XU62DaGgcpEJtuFBt11t78Hv9o94Wfububb+NFXXpXp64uul/U4nbU7Q7R5X1/CV7RsW299M8zSZKbHPwGBCiE61xhvvOQtwgUGWHymjHE/RcQZZxrMppAuP3IGVZ2npzrtj3UFqKnt41zKZEKKDb+xHdq5hTeVa8UKcp766PVWPmTidSdYhTLd9REqC8+LK2dRPUgrk5ZLf5b2033nP8rPPsj/sH9BQ28ZPtqG3I2kAVFSPAPCktirR7cKIIsI6jk+s48nOtUS39VJwszmkMxtDuqQ4L6eUnRi2hXRkZ8irpGYVWCN1C19Zvybc9B1UUg7RXptvBHsA9ym7WWR7M8XXjA1Hih9aZnWUXUyzDeKeJBwrlCjCuUbRANdOdWUfxZXTPsuycJMayl4Cfcf3KMcZYJlE7iAP9pxEsiOkA/Ns/YOm81Z9Y/2aVfYeZOEmv9sGstXekazcSPPzprZ/7G8zyTaU8spRbnc1b2XlIL/aPjLyxCDLjxQJes/Vaaquo5hyJlnnel2bwbuWySlP7F2olHIyWS1sqil/uOMFSVacPoF7anjdMoswJZo3LdNT9bi3kwOL8XdDdQuzbO8n6T6aHiooR8jHJUqrJ5O8TwnlFHXVnWTlBs9r/xHOdRTcZEvivfBGTGwZ88Biz/87o3xekWZuMU/vLsVWxVPhO8g6gUrqEcbYvvBZr+EiCzeTdcxm6hpaaYv8rivEefpYf+c96xSsOOmizY59TiZUU90X97eyh1HWLyjE+WSlJamKK6e5RznOPcpxtthfo4s2O+g+q+09WB/SlQJcYLptIF9Zvzat1RlrHcGnlu999imlnORTy/c+9/r8XGSDvQv9LFP5xvY1L1v+YbZtQIo+x8uWfwB4y/Jrottlsp7spjuWSLFAc7H8bv8IgJvY+MLZMlnHzMcl1oZ056g7Lw1jRt5qEg0rQ3oC0DL6A9bp5ZO9f1N1HZf0rKzVKxjL7lGOc7+6jUmuR3DGy1KhRPOA5glqiijneVFbzGZ3aRa5aybpfEmtDUxrdiVhBYkFJ32svwPwo7MZp8jtsz6/ctnvsXJzhbaWv5nuasxxPW+qptOlJ17XZm5VtOFI0jFtpsqhksppdunFU5a4ZNJwUVC5YPy7iHKepupaprsaE0mWoPuHEcUNQvyu87YiR+phvO/sAMBc+3sAXCeEX10PAjDd/jEAU2wR9HN0pJqyn3edHXEnsU5zb0g7AHrGdGe2u0GS9vHnF/sgABba36ZE1JREt91i74RNcTHe2TTBuqLKGWbaPwTwOc5vto+ooB6lr+M1prsaA54a9q+s3/C3uxafWccAUEw5QyfHWwHP/UDskJNyatoXfJ/QVgPwiLqBGup+AOqrO/jbfR8A3bRZVFMP0NXRGxdagv2zcBMnGtHY0jytSdFOW0Ary2IOufPT3dELFyp79KJ002ZzQC/EgtjPZWYnhhHW0Sx1VzbybHL8avuYUCWGmfYPOaXnoqBykbrqLh6JCdx61UTdaBSI748eyVPqSn52NUn0mlRw86b1NwB+cj3CcT1fstPq9ZK2iA7aPNo53knRccoox+mszeFr1zMc1fPHLtXJyxXOkSPF6UouOzH8a/dcS2WjJhKDNeg+aiIVN//Y3qK4epZqUWO4TLYUpysfl/jMOpqfXI8ay9woaLj8Xkf+9u9qmc3PriYc0Asn6ZwdtHk8oa2mXcw7Sbq3g+f+VFY5zja9JFWVg+zSi5mu5bjvydxi/lPsff8768hE83jy6FRWDnFQL4iGmxzKNY7q+QnnGpFk9buHFSeTbENY7y7LTNf9tNT+4y9XbWbZPwBgs7tUks++JLaF9qC7AKXU0/ynrsKNwoPaFh6OHh70N4grv5ryVpQpMHfd3sr4WzHP1p9y6nFqRI3mIuGAJ5/kVy5xRC/gd59RNk/5epm7sul+olNcOeNT9nlOW0o/6zQAv89gc4W+t+zfTFsX9HmdfDr/2d8AYJe7KOHKDfpZpzHJ9QjZuc5J8vjdK4fiqbR71fIX96meyoWejtcBuEc5wSPaBgDecb5m7DPL9gHhyg3KqcdY4q5KKNGEEEMu5RpdLH8So3vuB2FK8DzSQl3OZbKyxF2VR9X1bHeXNNbFr2TMzRUukD1J38bdSFrMU1k413lOXerTclMqQI34cMtoRlu/wF8rSSPNMy60mHouTdJZw1S7l1RFlLOMsX3Br7FBgtdC+9t8aJ1EWz/do80FiUfV9bxumcU424gkn9NfQcSGgxm2D+hnmZqM1Mdpqq7lcMhLDLRMwIozQbdbGw4KcsFnmeqngkAzLdOUxLvuZuMGE61DeV77jy+t39LLMpNfbB8nOc3VlP0ssfWhiboh0e2CBYzm258eYNsn1ZU+Y9t8Pmcyuig/oG5N0AUylKiALW0WnHxg+YmH1I201v5hl/0V6qs7jfXTbR8xwDqZCOu4oOdupq5hZ8irdNNmJbpdAeVigmUVAgxBGGr9gf9ZlvCoup4s3DQqOeJXHhVRzrHE1oe22gJj2Ve2b4OmObXYYvNiGT/dT8sFaCmqoHo+8zPqcmNZe20Bj2objKAc4F71sM9+4Vz3GeuqmK7XPFzhQ8tEyirHgqY5H5eoa/qtk8P8UDcXwt+2/sqj2gYe8jN20U4MO0I6sNXeMUXnTAutLJ4Wq5LqGebZ32WBvR/11R28bf0lNhCO+24rKwdpr82nibqRJ7TVDLOO9TnWu5bJfG79jmAt8KFKXEVdwdhr4R41Lt9ouKij7PLpKXOfutv4+w/b+7xt/YWPreMTPY/5HmJLYS+wIspZNFxEWMdRWj3Fp5axwXfy4w/bAFpaljLeOsxYNtAykXUh3Whhyv9pzfwMfl5bylxbf0opibeUmp9D8Z+NxVVPT4daqv8WvWBe1v6mvrqdj6wTaKht4wfTczq/colt9o58bEn8dwb42vY17S0LmG17P8nnHmD9merqfjpY5id5nx+snzHbPoCZtg+YZf+Acda4QNuc3+IX+gFyKEnrLQae51ILdXnA1s+H1E38aX+fabZBrLL3YKm9Dx21uWwNeY3+AXqIPK6upq66ix6WP1hge4culjlGUA5gTcFQoFKqpxdUI20rD8aWH1/S/vW7bR1lV8KyhLmFyafF/M4JzL0Vwveberj9ZevHf/Y3ArZ0e2XnOvXUHZRSTvKGZTr/2d+gr+UXY31Z07Ozl/Y7S229gvYgAxhj/ZxptkFJamRKynhriylvVFDjnq07QjqwMqSnz+e04KS6ss+n3Kb4LVOb78lx68MVTxxTQTlCb8sMOlvm+lS6B2rJzkWkT2/fYsoZvrR9xwTbMJ5SV/G97QujkRB8y609tJlsCOlKO1P5Kb17atxuEpinBlM+/8b6FSNso/ncOspY5m+MkwUnLS1LeUxbR7EEXQf1RGvGU0Nyxl01VLfwluUXiihxD6aKymFGW7/wCbwqqwcT7Gv+HPn9BEHBmB+wdmJopq7hRW0xNdT9dLH8mezjAYyxfQnAK5a/WWfvymLbG5h/xD9sA1gV8jqVlEN+0+FlvvG4g7RUd7P8QSNtK59Zx9AgNugtrFwIuH38m/gE26eUUM/4FJb80U23SgU3hfGt2EmsKyR4usV9bfvGZ2yblkhhML5O2hy+sY6kprKHSbahLLL39Vn/j70vS+19uFdJmFde1JbwquUvfrR9xmDrj9gUl08Q7g0ckjKZ2aexYxHftibeRSqpLd9mhZVz7AjpwCZ7Z57X/mOrvZNPUPmuZTIl1DN8bJ3os583gFVxU1fdmeJx8w+qm2isbg66nfkBHEI0Bbjgt4LJzJwncipXE6w3//p5ucTWkE4ssL1jLDPnj8+so2lvWcDf9ncIZm1Id6bZPknRZEfmPF9KOUVn7U+fniF2Pz1DSsR25bcrTsKIYp6tPwMsk5J97kApCuRe5WCyhjaYnw01lH201JYQzjX+tL/Ph9ZJdLf84Xe/1yxzeVZbThnlhM9wjeTqoc3iF/sgvrd+biwz3w9yx+aRuqoneK+p7PG5d1VQjjDI8iP5TUOBPHnM8x0V5AJttQVBr4UH1U0st/f2CaYbaCmbGCtLbOtOaTWuwvwVi2cysXdiW8QSU1PZwxfWb8nLZe5TdlNH2RVgS51hljG8rs3wu7ahaV6BCOs4KqlH+Mya+BwmVlMFsPlaNn/nN7AH/Qzx1VF2Mcg6gSm2CPL56el1n7qXMCWatpbgc5NUUzy9V7IEaEXLwVXychkFNwMsk3jaVBlivlYV3FRT9mPDQXHldII5W+6P/f2rxZY7vP+24PQJXvxVQJvvczYcdNLmcI9ynH6WqRwOecmnG/gr2gK+tH3nM3bYipNfbB/T3zKZFtoKACqrh43P/L7VE5B3tsxFwZ1gbK9difucFiXhPbmIEvfcLqccpZs2K0VzaPh7xoOnB9YPthEU4AKFr++Aw/EqpKJM6b0j3mPue891mHp2lFA9DQHNtTUJ9jJfN2WV40y1DeZf+1v0tMwC4FFtg2nbuHP0sf5OMfUcnSxzjWWBnq1NtfXUVXdRMt7wsfg6aHPZFNKFVzX/lVNZuUFjdTOhQfLBDPtA4+8PLT8x0/6hz7PN/LzUcCUoi/gL3M3M14b5M+ckEhU3RZRzbAzpwt+2uLJfPtP939+wQHM+fcvqGTbzkan8lBZzG2Rk0pU9lTXUPF05zRe0C42yyjHO69mN7jXmbGbO3P0tk3lKW8UkZ5MUnb+euoNm6lqGOlsF7MbrSVPSAxJv968CrriLa579XcA3GC/kJ9A03/jMN4Sm6lquE8pyd2WqKAc4o+fkPNmpqexli17a6JKmKnE3iQ8sk2ht8T8WKKVyKNfJoVxHQTfS5209fFpbyQ6np7tNuJ/xWrlMgUugB6BXeVOhwvyZ8nGJSMKIMhWmemm/097yF0/HDDK6X2X1O25Kp6G6ld3uYn7TMdjyIy9Z/uXNmC787m4IxP89EiqgJBxLH6y21ew9q6fb1hN+HoIQVxnxiLaB7U7f7nqFlKSNx8qmBB9D5q8ixZ+UBOb3xFZGhSnRRmvy99bPqRL9AxB4iMAue3s+df4PNyofWCex2l2B/8UkHJsVShTV1f2sdZdPMDQEYHzsJDnloib4dN0M54ZPN1BzjvzP3of8ymUGO14ylmm40HATYzqHb8VOwt/ajUo/y1Qi9TAe09YCvgGOeZ971UMJ9g+mjrqLVe5KAde/ov3FM9py2sXEBfveWn2Iy39ZTXlE93Ntmpd8bf2aiuoRKqpHGOR8Oclpravu5JSey6eLZGftT161zKdlzIfk4xJ79GKUUE7TRvuHca5mzLF7WhGT2r3RHGBMsH1KuHKTJ9TVxrLyfnp3mOcPaK8toLVlEYMcrRnnap7kz/aOZSqX9ay8HBuINdS24c3W/q4tHYUfrcOpr+30Odd8u2euAXMrfEnlNL/ZPmKU80k6W+aST7lMbXW3cc8wfzfvWyYRzg3yx96XvM/XtBLsPg5x3VRzcM1omawUNY7rhPpsV005wAuW/wCY7mpES+0/prgeNrpo+suX2WPHB9tx+DwTvMy9U6qpB1jorhW7PO43cekafSy/YcPBp85Wpr11LLgS3FNU3Nxreo4Hq3zNRSQ/24bwm6shv7gaU1I5xXa9JN6rKtgzYnNIZ8AzvCd+C7mdGFppi/jXVZ1nteW8Y53GQldNo5ttMC9qixlk+ZFejh7GskfU9fznruqznbn800X7kzesv/Eecfluju1dGsV8CWBUgpqD6YfVjdRRd1NH3c1MV+JDlL6yfsOT2mraxPRnubsyEPw7zmZ63i+w94s9v5MvnM8DcL+6jZ6WGfR3JN7jJ1h+rqAepdv+4bAfKFY/bkU6tZh302aRX7nEh85XiLtL65RSTnEoQFf0Z9WlDLD+TIeYuCFW/sq35m/8XuUgH1gnMczxorHMW+4LxF++Nv+OwX5Tf/vbiWGB7R2WuKsalYMfWCfxo6tZgm0n2IZRS93rd4haIN4x3N5jg+/3MMs2gMrqYZymhiWFxPtZVYqdzwhAM5VlN4V0YY27PPNdtQFPzy+vYN+NjsJs23ssdPkOcW2pLaEgF7FEtQDuTfQYdxMJzFOBHiTTlVBOG61G3kKHuVuM+QHdObYG7lXLXz7HUHAH7Hps5m3pjCSMz5wvBtzOiYbn8kt6TVRpP93szK2+FZW4CzYfl3hOW8aC2IID+HaX8bZaN40eymy7Jzj51vkU3S2z+ct1H10cfQDfSosXtcVJTqtXWeUYD6ub+MH1uM8kMPGp6OTlgs82bbW/iXC2BqCcau6O6/neOptm6gwW4N2n+n/Fx9qQ7gCUiJpMdWU/u/Wixrj1tyy/8rrD093HX636S9q/Cbp1mx9IL1k83dj6Wafye3RD43N6+fvl/U02mJwWc/90cnAt3rjHtK0BTWo6k1IQT4qkdPG3Ki6jJQU8rYz+jLF+QUNtG185W/C584WAx7MTYwTmo6xf0kxbx+PREcZ6c0HAO9/BQ6aW9jrqLsZZP2O9u6yxzNxa5i8AK6KcT7SnirnSKVgB3fw5vNy6muhY1oHWnwB80jDY+mOC7aoqB+KOGeQ3NrdOJfU+W145yjTbJ4BvINnf6hles9TuuX/tcRcxuv55hyclRy7iKv/CYysbGmlxLa3m71vDRR11l08Fqbcic4B1shEsK7jpps1mvbtcwPN2jf1+L+oJx8iG+pkA0o1Cfc3Ta+Ql7d8ElQAVlcPG3xHWceRRIhlguhb8VeTl5yIdYwO383p4wLQmJgs3uYEdHRUrzkSfAeA/WA6krKlLZ1HlHLv1Yj7rw5S4VqjJtghKq6eop+7kJcf7sedKqLR6ip+sQ3lA207tqG85S06f9eZrspa6xxSYx923NcVFL4unlX6CsykuNK6QhR+tw6iqHqRe9NdUUg5zlTAe09bxmjbHZ0hDsC63XS2zqage4QN1Eq21fyitnuK1mD7G3A7+7r2PqWv51Po9j0fHvV2leuy8EGbtLZ7uq6ctOY17alKDcoBPY4d0eMcNA7SxLDLmEfEyP6/9DevzDguAuJZ4M3Pla7CZrp+MnQvjNW1OkgNz83Xt1UjdQl7LJaa4HjbeUvOt9atEj6OjkoOrWHEZ8yeYf1+fHjfXTT03fcaY374Wc28PtymuhwG4pGejhbacd61T+c75lN99Po99U455IjhzZa0/3grS3+xJH054f4CWXhU3bpQkPe/Mz2yA1y0zKaGe4RXV9/V/Gi4KcJGsyk0W2PsxzdnYGKISaHiCWWN1E9f1UL/rzHe4yrFD08zlSxU34Vwn2mfOi6TdF+uou/nLlXAuFN9yQUJVYivxq8SrzO+kzaWseoKpUdWSdP67hQTmqeRtyzS/YzrBd7ZGryrqAT9bxjEHSPcpuxlt+4JfXA8yzPm/BNtquGisbmaj+x5jWcLu8b4aqDt4y/IrHznaMsPdMEkTu4QkoyvVBNswKqpHeMq90ljmryt7A1PX1e4Wz8ySj2nrKO08gRWXzwPMX3CanWtcCTDBCmBUiLxjncYQRyvGuJ70u119dQeT4s2iHmKqIdfjbdtZm+PTXSrYTTmrknhXzS7an/SzTmOzu7SxzF8rvZm/sdb+gpC8SlzBwTcwT5hm8zwBhTjPSfIEHGPeUltCeeUYg5xtSOzG/bl1FM9qy2kV856xTDf+V+Ejy3gKKJc4oBcKeIz4QomimnqA9e5y7Atpa7Q+Z+MGVwnz+Zx9LdMI5wYDnK8m+fjJZUliC31SeFsFX9L+TTQwb6GtwIqTZe4qNNM8r40xV+hV9tNirZgekE3UjYQqMcbkjOAZo9dSW0K0bktyYA2ervKllFM+++T20xXerJJyiGbaWp9a8jrqLnpZfudjZ1smuh4ljGi/PX/8BYdm5oJ3sMA8r6kyorhyhsN6wUS396T9cNBtwHcSPN/eKJ68HywICtRV3Z+elplGQObPx5bxtLUs5CtnC6OrZjD+Aghzy6GXOaD111plvodkSeIQjmymwnUeJfmvaiymnGGpvQ9LXFV539meJbY3+CXeBHmeQrUpr+hJD8zNwVkPyyx6xFailleOcp+6m8OmFj5vr5L62k4KOC5QVDkXMFd6r8eOlnmUV44ywNne6JVhHnt8RY/rHWP16bodd+Sa6l6+sn7DSncl47gttf+MCi5/ggWN5l4c3s/1gXUSf0d7CuT+7hujYyviV4T0MpYldn/x13MrNemm3zlQF+TsXAvYs9CcdvP9MzEOLBRVznBezx50SJE/1dQDVFMP8JIlroEij5J4pYBOXA+Fl2Le5W3LNIY4WhvrfXp3uU2Vy9Gme3cqtZgX5AJl1BMsc1chlCh+tH7GP+4afrctoZw2Gm+8ulkSn3ncPAziM+sYXtPmMMI06bKGmyLKWb9Dm5KijJ+Z8u04OBjSBoCpzsQn33xBW0Jny1zecnRmjqsuddWd9Ahwfz8Q4ttz63+WJcbfliTM0ZGUV88FEkY0m0M6E6XHBebJeTuU+fstoxxnv17E53pJTrmibGxPq2zO5A+DvZNJYJ4KdGdM0JtGfOaCio5CM3WNz4Pc/HAcYJ1EbuUq3Syz/Qbmr2h/+bQ+QPCu6o/FFuQ/t43mYHQhfrV9xHDni2zXS1Jd2c93rqeIH2xZgrQKmi837+u7zN2Dmsd2fTULdJP0jk1e7Krqd71XYeU8nbU5/OpqxGG9YKKtIv2tUwMG5mOt/sduW3CiovsU3qbYIhJsd6tzArwR+zqaaqYKm0baVpq4NrDGXcFn2z6W33gk4CRwiRcsNcW3xeVj6wQ+crRlnrsugM9EXWXUE5x05yFciXsFj0VxGz+0952yO9zFKaOeZJLzEb/nfFbzjF/rpsU9hKoqB9ho70yEszXtYrvLBvutzTbbX/OZKb+uuoum6lrG2L7ka2eLeK0Cnmvze1dzjun5MefUsil4bY6/b9iuOBhrHcFuvWjQsWRJFewajhvDHnftmwta/rr8mx+KxQNMwuf9XZMznOYX2yCqqgcD/oaVlEPUUPfxs6uJ0SLtnQW/imm+Ae+44Y+sE7lHOU4byyKejR5ImBLNAXdcxU2wHipO03p/raDmgkYu00RQYaaAP4RoYrD6nKsgF8irXPap4EgJzz1FCVqh4y8IDiSxoBwwxgcnNSgH/0GLv3u2rivGhaGjMNk6mEjCjPXmzxk8KEl6T65FtjfZoZegp+N1HlXXcVTPb7Rc/y+2h1VjbQuv6XOxKO4EQ6E22V/j19i3EIBvvgohmhm2gfzgbMaM2KFAZnlNlQXmXh9/xXY9DmR1iGcm5DXuxN+K8lps77n/tDd4LaYPNpwcNmaRx2e2bXOFqXnY09PaSiyK22cIQLDJGOO3WsXn7/laRDlPY3Uz691lfbq3rrT3YKupstksrefRMZtr689XzmeMf5vvrYHy45aQ1/wuh6QPlTIro5xgmb0Pp/WczHHVTfb+/gQrk5nv995yyy+myXt9Ai6X6bqOumxabn6Pecqtis33L8f0o7xylHraTupp/if8LG2aMNmtK357EMQXv5W8rHrCJ7i/Vz3EckvvgPuXSMFz2zzfTbAJlb29YT+zjvGZUDW5tFt89gTb2/s9mBumkvL9e3U1xULdLX/wjLaCtx2djGXJCcy9CtxM2LvmbiaBeWpwJ7026R7lOM9r//m0TtdU9vKFbZTPduaHRbAavt5+CmQqOqOtX7DOXZZxrubYcPiMJTUbYh2LTXEZ4zMBjul5Oa7nZY9e1FiWPw1qsesE6M7rlTdIjfCn1u+prB6mtfYPg52tGWYdS6+YbvS1/pogoE1MSIAC8L+2N8mlXOVmkAl1Aj3cn1JXJqm2MdCD/gfbCNaZuhpD4gXw1to/jHM9HnC9eXZl70PrO9tXPB5dgErqYZ/A3Zsmc7DiryDg7Ur2sLox4HnBdwZo79hM8wMqOQU1f6+v+yR2ZujXLbNw+Wn58h7f3LrkRKO6ss/nNUmB3qLgFejB8oi2gUdIerfL+ApzjpOmV+45k/BqoviC5bXspvccP6wlnK3cLDkz8FeNHaP6YIDu2t4g3IqLMsoJJsd2VYTA44bbxAZRv9o+TtBbJtjM3u4ggXmrIN0Bs3PNKJiXivqZEdZRrHJXNGY//9Zvt8qk59+BlonUVXf6nWMgI/F3TfrrOWXulZBPuZzgdXnmvBTseabEDg5LShBQWj1FaU7xj6uG8eaDe6J+ooG6zadXRcBWUeUGnSzz/H6O8dbhVFSP8LltNDOiEgbmZkl5jVh89yn+hzf5831sN91OMW8Yy0oopxlv/ZSJrkfZ4S5hLH/D8pvxd1NtfYJjmVtcUyJHgMkEJ9iGJahsKKRcpJDmv7UrJcFtSnneef2l8e8S6hl22V8hVInhih4WeMd4nlOX8pi2lhXu5I939XaPL6BcMoZo3KpggXmwZ6rPfdRpqsi9bprvxZm6XdkbqDuICvJqwChTd+/kBIWJCZbfHk3GkAkvc1ngdrwi9HbokYyKW3+ymypInomdGNH81pDCSZxLyMytJP/+eieTwDw1BHqRuR8L7W8nWPa8tjTBMvOMpuaCTA9tJpXVQ3Rz9DIKA/5axuqrO8irXOExbR2L3dX51/4Wi1zV/abJ36s53rFOo4hy3rdrdZBxO+FJmJQrvsZBxl3Gfz1TfN4xMtmVG8bFP9L2HQBFtGUJtg80Q24g3tfVZQ3S/fIxbZ3fgPgr2zdJOk9itaD3JeP1N95XpsTXUlvCVT3MZyZgM+9kfmbeh77vq44ctNfms9pdMcH2wR5MwV7jc6vjvc2tVv6+z9xE0kJbwWx33CQ35dVjxnu9vRppWzmsvRR/d0NKanyDeVpdzkjbd/zmigsAXLrKP7a3/HahCyRYwFpeTby1zCwpk+wl1wdWz+yw3vkPksLfEJZgk0CahxX5C8wDVTJ6tzVXMk2wfkpDbZtRyACoZJpPwys5+cLbev2Mn3tURmIu+NdWdvGedbIx+aGZeUyuv2/B/Btag7xaspW2mLcsv/jtHRaIOT/0tvxm9JLxMlcKJiZuCJju05qn4uZdy2Q2xKsk9XIG6cHh5dKVW2rxMhdqvfMZPKhtoX5U3FjjokGGsd0q74R2/tQxvUovmORU/KUFb57IHqRcYzYithK6oprw+k8PwV6rFqwxxed54TDd7wO0mKcGBbfP3Bn+FE+lXmdmaVERdCfOFR5sHo2UVFAkR/0AvSQSI4F5Gvj2228ZPnw4p0+fpmrVqnz99dfUrl37dpz6tgg2+VswwV77UsI0u6H3VQK1XbsTnb3Y3HL2r90zW2WgFjJ/ta7eV6NVCzIWPr4m6gYKJvIasPT2pvW34BulQF11F9mUG8xwPZAmx0+OfpapbDe1oEBc9+RlrqTX9DdSt9DX8otPsNhBm5/i1xQFkyXIOPxgglUMjbJ9SX7lMs+5E1aEpbc+Fs+Ef+ZKuhzKtWRXdgWbuyI5vJMW3YnMFVTmCp8+luk8ra7ksmn2erOiylmKKmd9eo74a9EPNo7aH39dMlPyvuLbyfyZfjV1gU2M/zdIJJ137oyh1h+SvI95Aq4XtCUJ1j+XxAoQbwXCU+pKn+WPqWvpaJlPR/y3dNpxUEvZzUbdf+Du5UY1AtKUtAQGmudFM1V23M6W6FthHjd7p0nsVae3U7Dxxk9rKxNdbzP3FHQEqKBwpWxMdmKCvXavXRJey5dcyZkjKanMZXORdtxK5mpDTvNP+8svv/DGG28wevRo6tSpw5dffknTpk3Zs2cP+fLlS+vT3xaK+/YXrmJ0C6AzIsB7T3OYxgUHYwnSgpEcwd6zfbfydmNur/0VfOM0ltis2cmZpMzblfhddaqxLK2Ccgg+YeGt8s5M7u0FkVJKKlaTeyfkivQzg6o9GROueOVNwSRZdztzYN7LMjPRbb1dhje6yyS6nbl1PAdXuUy2FM20nNZ5/lYFa932x5aKz5OkMr+yL+ctvLfdq53p9ULgf0Zms0e0jTyibQz6KqNb7WsTaKiKuYInLXr0iIzpVscb5/fzzvoEUvl1aSl5RWlquFu6mt+q8kHmmMiIMltgnuZXyOeff06nTp1o3749FStWZPTo0YSFhfHjjwlfcXOn0tMhMO9mmU1BLia5JSAxRVIw5kP4l5xucekhZ5CZstNTWs/Em1oeVROO20wpb8uBv5bx5Ez8JRKT/JqUGn5e5eR7xLgCcWN1Cyvsr/OkuirZ50lOl36RNP6GPtyqpP5O3td9BXKrlRaBKtHNvd4CzZciRIqkclf21HpFqUiZYMNHM6LM1pU9TQPzmJgYNmzYQJMmcbP7qqpKkyZNWLUqYSEmOjqayMhIn//uCPrtD8wbqNupGGT8tUg98buG36mSM8ZY+BdsrgWRsVRT93M45CUOh8TNG3BKz3VLxzRPcvel7TsKKxeM8aep6bieJ9H10XrikyiJlEnO+8xT09pE3i0PgYc+BJsETIgUS8Hkb6FEkTXA616DvW1EiPjOhN0TfKO7SJpeIefPn8flcpE/f36f5fnz5+f06YSTOwwZMoTs2bMb/xUtWjTBNhmRrt/+MV0hioNxmbTbeHq4U8btBXNBz5beSRDitvL31oqCyp3xXtSrfoY4mO3Ui9+mlGQeKu6gE1WmFZeeeMtQoK7sX1q/TYvkCOH7urQk1VfpbLF3YntIR3poM/nD9j6FOWdaK4G5SJ6dORqndxJuqwx1hfTv358rV64Y/x07dme07inpEJhnRsFaj9LSrc4gG5VBWrZyZ+Cu7OLW3dRtwTcSd4xgExblv0MqGO4kj6TiUJXkCvReZ69AgXlZNeFM+UKkimS2mNtxGEM23rJOp6p6kPaWuLl33LrCNT0kVZMo7l5OXQUlQ4WqaS5NR9TnyZMHTdM4c8Z35sIzZ85QoECBBNvb7Xbs9sTfF50R6W4JzO92t9piLt23xO3gQCPxNlaRXNG6BbuS/In4UkPJILP+FpLAPNWZ33ed0diCvANeiFQXb4x5hOUHKqpHaBnzIQ4/IURLLeHr9IqbJrl0o3CdkKCvoL3TROtWmRcmDTjRUDLZvARpGi3YbDZq1qzJokVx7xh1u90sWrSIevXqpeWpbyt3tkLpnYRMIT27k5e8xfdq+nuACZHakvt6NRHccT1veidBCEAmdhPpIN6s7C9Z/qWaeoBGasJJxMoox/nEOj7B8kdM78ZW0TmZjr0fUyrYsCIJytNGZiw7p3kz3htvvMHYsWOZOHEiu3btomvXrly/fp327dun9alvm1t9j7lImvQcF5qSd86ahQeYCEUIkbGZX8UlRHrKTtJfgyrELSn9sOf/Xf67sof5afFOyptVyqnHqB7krRcZUVq8Bz0jOqdnT+8k+Mim3EzVV9TeCdK8KuLFF1/k3LlzfPDBB5w+fZpq1arx119/JZgQToi72a0G9kIIITK3h7VN6Z0Ecbd7fSM4bsCVE3BgUcD3mIcovoHqA+pWJtmGBj3849raVEnm7Wa9xVcd3inyKlfSOwmZ3m3pI9CjRw969OhxO04lhBBCCCGESK6s+cCeDa6f9/w7QIt5YeWCz7+TEpQLIYKTGalSgw7TnQ3TOxUpckLPnd5JECLV7HIXY7W7QnonQwghhLiz2LN7gnIAS+xEzGd3wpoxsGYMVU9MNTbtZZlBPoJ3XRfiVvzrqpbJpn6TwDzVlFRvbXKw5GgQNZIxzuYJlt/Qkz+jfZuYd1MjSeIOUD/qK7/Lt7pL3uaUpJ1mMUPZ6i6V3slIVaf0XLf9nNEZ5PV+aWGi85FE1zv1wI/Fi3rW1E6OyKBGOp/1+bdD13DpwYuIQx3/Y4WrUrLPd8BdMNn7CJGqok3dmO3hcX/Pfxvmv80DB0b4bL7K3oMs3MQS4DV+QtyqG4RkujHmEpinAh2ope5N8vbVo0b7XT7Q0TbBsnpRX7PdXYLV7goccuenRfTHnCAvnzpbsdJVkf1uz4zwHWLe5MmYT/wed6e7OCWiJvtdZy6A/+56IMmfwZu2jKZNTH/6OTpSOeqHBOtGOZ9MdN9lrnvTKlmJ6ut4ze/yM3qOFB3viDuf3+Ux+A+2nooZzE9BgpVbtTxIQXWPu8gtHX+QozX3xv7m2+/AioZo3cpsV8I3VZzXwxnkaJNm5/3R+Zjf5YOdL6XZOUtG/cwad/k0O34wHzrbM8dVB4Cj7ry8EdPFZ/2zMR8l2Cdat/JI9DC+cz6dYN113c5SV2W/53oz3rEzk9s1idD1FFRIez0cPTxBsN0w+gtKRE3hC+fzPss7Od5kq146wTEOu33ny1nprsQXzueSnZbkTDK42V2Ka3oIzaMH84XjOQ678/Ox4+VknzO5Ar1/evNdVhl6JzuZWhW5+SvBQ+9DpWeM/85k832Oa4rOjpAO7A9JWHYVQqSMBOappGX0B8bfX8araTfrHNObS4QnWD7HVYdJrkd4x9GJua7axvJT5OaJmAj+FzOAB2O+YLNeBgA3Ki853qdJzGeUiJrCIndNDuiFGeZ4IcGx17vLQoDOICfJw2eOlnzoaMebjq7846purGsd09/vPlOdD1IzahSnSLwbfF1T4L7OXZbfXfdzXbdTOeqHZFcCJEXL6A9Y7q7MNNdDXCXMZ90CVy2GOV/kedPv5PWhox2Vo37gZUfw3gMb3WX8Ll/oqpmyRAMxuu9UD+85XqVE1BTqRH8XcJ8lrqp+l5eImsLzMQMTLO8e05PzhPOHq77f/QY62/GWo7PPsk0BPqvZed2Tl4f7yXcH3AWZ5GxC3aiv6eLoE/AYPzofC7h+t7to0DQAjHM151rsb75fD/z6wkB5OjlSswX7XUcHesV044WYAfR0+M7DUSbqJ+pGf8N8d22/PWRSw8fOtjSPjuBSvJbgs3pOv9v3junmd/mfrrrG3584WvNkdFwlYT9HR6pGfc88V21ej+mBjsqLMQMSHONXZyNejul3y5U0wxwvsNJV0WfZJGcTACP/93D0okTUZBrGjGSGuyEfOtoBMNb5ODv0ErH3zDjloiewTy9CPXVngvMtc1fhVUffBMu/cz7F7+4HeDr642Slf77rvmRtH8jZFFbsJeaZ6ISVFgBzXHVZ6KpJq5j3mOJ8kGGOF7gvehRDHK2Sdfz/XFUoETUlydsPcrRhrinvJUeJqMkc0Auzwu1bIWt+PZ7bFLQvcVejp6M705yNeSw6bjztSOezPpXt1wlhvV7eb+UweJ5FgayLl+8C+dbZgnujf2SHXpKRrudoHPNFwIqQ5tGDk3TMpJjt5/lxwF2QzjFvpOh4J/VcOHTN77pHooel6Ji3Kim9ItLCWOfjjHE2p3xUwteNJUf96G/4KEAlzQU9W+I7v/Rr3N+KAg37QssJxn8bir16S2mLL1CF5q1Y5KrOOT1hGVvcfoHKm8lxn7qbRmX9NzbdrSQwTwW6Duv08pSImkz5qPF86XzeaJExqxE1mgXu2gmW1436mh6OXrjQ+MX1IMOdLwLwdwqCPfM7/zrFvMFMVwOGOhMWjm7odqM15xvXM0x0NQUgqxL3CowV7sp+C+J79KJcwFMIaB49mE4xb/gNVk6T26hJn+5qxJuOblSKHs9VwriQghvnDNf9fgtt7zva0zR6KOt031a4ebEVHJvdpenseAMdlfV6wpa6Sa5HjEC+UtS4gF1Zxzub8q6jI8fjvYOze0xPOjneTPbn8VroruXTRTZYgfqJ6E9409GFne7iRDhaMdzxAstdlSgV9TMA58hhBCJeh/UCgEIvRw8qRP1Iz5jufOt8isejIwBPRc9vrkbG9rNd9Xgm5iMWumr4HOfh6OHG30tcVWkWPZQSUVP41tXCZ7tWMe/xZMxgBjhf5TS5uUYYdaK+8dnmPcerbHaX4lvn0xzSC/rtMfJYzKc08VNAMxfaPnG09lm3Uy/BW47OvBTzLu85XmWcsxmtYt6jUtQ4Vrgr0y2mJwAzXQ0SHDeYSD2MetHJ6ylySc9K+5i+VI76gW+dTxnLz+g5WOiqyR/u+9milwEU2sf05bKehVdj3sKJBScWdFSGOFtTImpykuey8H7Xh0yteef0cKMixWyHXoLG0Z/7LFvkrpFgO4BZ7vsTLBvtfJKejh5UihpHzahR/OBqzj69sLF+husBrpCVbo7e/On2PqgVn54APWN68LazM8vcVXgiJiJFvVdaxbzHmzFdGON6ktaOd6ka9T09Y7pTNep7PnG2oVPMG/R3dDTtEVcAn+hqSumoSQx2tsGFxvMxA4n0eW+tZ9u+psqrvo7XWOcuy4eOdjhN991+jo6UiJrCMOf/AIUtehmqRH2fpM8w1vk4XR29E1SSBTPM8aLx9xJXVX5zNaRF9MdEmALjpBRWT+q5OOzOzxp3eZ+8A9Atpic79eLGvx+N/pSxzsdjn1+ee+AqdyXedXbiu9j7wQq3/54y691l6RX7bDHng0VuT8VwjClYaxn9QcBg/TdXQwY5X2ZQvHvAVndJFrpqUibqp4CVmN7f9E1HV8Y4m/OBox1PRn+C21Qsiv82jWN6fvo5X2O3XsxYZlOcRJKFk3ouLutZjMA+fuVwhKMVHzleprfD87lv6HZ6xLzus02HmLf41PE/trhLBQxa3nF04h8/1+cC933MdtXjbUcnVpkqpnboqdODaLTzCT5xtknQw2S/Xjjgu4bfdwR+LW6L6I9pFj2UxtGf83pMD5+u/MMdLyQoU6x3l01Q4WYWqLLfX4+jxIaznCJ3iirHdiWxEjmQwc42DHG2Jgp7wB6OSVVUOWf8/YOzGe872lMzahSPRX/qs90ad3neKj4dOiyE3tuhbNMgR069t8s0jx7MX37Kw8nhLz9scZfmvujRfoeGPBw9nK+cLW7pnEnlzfujnU/ySoxvxW3nmN6MczbjY8fLlI8a77dBLbUEy5erXBXZcgs9XsY6Hzf+Nv8ez0V/mKBsBp5eav684+jEHD+VrGFEc2/hzFXRkvne3J6mFKLwdKvr7+jEYb0AnbU5bNdL8qXzOS6aWspfinmXtyy/8q6jI6fjtTwf1gtSJWosVwkluZa5qwBTOK+Hs9Bdi4XuuNr5N2K68LltNPWjvuI0uXwKIF7Z4r1ve5b7frI4ohhs/dFYdtFU67pDL8kOvSQLoz3nORzi2wW2dcx7lFeOslG/x2f5DNcDPKWt5LieN8nDALxdlBe6avKItgHwPFh+dvl/yPZzdGK1uwLz4l3snWN6k0eJ5Iyek6t6mM/3cJ1QHo0Zxr/2twBPF+kSyhlGOZ/iJJ6A/P7or3w+Z0omG+sY8ybV1P1sdpfhBiHUjf6WB9VNVFUPstDtv0KmRtRorhJmFIIejxlirIsfGH/pfI571cPc1G0scldnh17CWHeTEGa7G4A74Tkejf6UJuoGxrkeBxQ6Od6igWsbk21DeDF6AKdMkwV2c/TiBnFdG0c7n6SL5U/+cNVnlZ8C+Rlyscx1Lw9o2wGY7GrCZFdcBcIE12NMdT3EOntXwpWbxvL9ehFejunHUOtYDrkLsEEvyyG9gLH+V1fjBOfyVjKsJGGAN89dlypR9xJJFgY62vGZdYyRn7ymOxvS0rI0wb4PRY8gUO+TQG5iY3FswDHC+QJzXPXYoxfFipNobD7bLnZXp1r09wHOodDX2YVQJYYntNUBz9c8ejBnyEW1qDHcIIS9IZ7W4P3uIrRyvJ/gGgW4QhbWuMtTR93NP67qOLAwwPEKg6wTjG2mOB9MsN9CVw2j4u86oVyPvWdFYadp9FB0lIBDKKa6HmKA1VOZNNsdV7PuwEIPR08Wqm+TT7kMQNWo7+lm+QMdhaLKWZr7eeXOXncRVhGX766Q1ZPPvWl1B26pBHDh23L3tqMzo21f+gR9FwmnXNQEHFhwozLdT97b66fF33ydeB10F6BUvLlJBjs9QcRvrkac0nMx2RZ3je9wF6eSesRv2ie4mrLKXZGTem7OEBfQfO96Eh2Fcupxvna24D+7p2VzkKM1/SzT+MbZgv16Yb61fRW7/GUWumviQqWUcorvrCMppx4H4LSei2hsNI8ejIrOXr2okd5ANNNNZrGrKg9qWzjqzkvLmA/QUVkeVZkLhLPUXYU66i4muzzvTq4RPYbHtHXYcRgVrtvdJbhXPUzXmF6EEo2OwhU8FZrjXM0ZYPUEM984n+YzZ1xFxduO1+iuz6KdZaHfNJ4jB0OcCQuQSbXVXQoXGo9ED0fD7XNNe59VK10V+d4VN5TKE3h5rvF6zp20tixijqsOkWRllOspRrk8FXjf8BVFlbNUVQ8a+/7iSngdgmeoUk+HJ9Bvqf3ns857bZv//ZajM69qf3FEz89A60+Ap7J0kas6Z8jJoZC43/ZDRzuj8n6GuyEf6xPIqkSx3FWJAY72OAO08Ux3NSJSDyPCOo7XHa/znXUkobGv2fL2/rtCVk6487IqphLrQ7oC8Ie7Pi7TMTe7S9Ex5k0uk43DWsL713PRH2JXHDynLfNZ/rvrAca5HjfuMwB/ue7jQ2d7jur5jDxjNs7ZjPGux5jKYOppCXvIBPJCzIc0VjeTXbnOJ9a4Vu9d7qJUUI/53ee4noe8XKZzgh5jCm87OlFKOcVxPa/P8fxpH9OX+uoOxscOTTqix1WqfeL0bT3vFtOTT61jGehox2x3ffKftUDRpAbIyQvMxzub0t6yIMHyc3o4O/Xi7HcVprByju6W2ck6rld/Z0f+03x7ayxxeyrimsZ8ih0HFly8bfmF310PcEAvzCjnU1RUjtAkGa8evC/qOzRclFZP8qy23CefHXXnpZgaVxFSMepH435vLpuWiJpCKFEoeJ4H5ka671wt2KTfw1Rb0nq3mJ/NU50Pco7s9LTM8rvt4zFDKMhF+lh+Y7TrSbpa/qS6so8CykUinK2Z7mqEHQdb7J0SVERudpeiWuy956C7AH+669PLMsNnm8FOT6+l3MoVdruLMVt9n19djdmglwN0Zrju56Zup7VlEeCp+DrsKkBDbZtxjCeiP2G7XopremiCss0pPRf5M9n0b4qu6xn2BcuRkZFkz56dK1euEB6ecWtMrtxwUPXjvwOs1UluQf5WlVZOcFbPmaDGPin6WabSxfInp/Wc1I3+FoB22gI+sk4EPC2pfRzdEhRivb63juBRbQOrXBVp5Xg/Sef0FyjEN9r5JJ85W8a2TOlouLlP3cM2d0kjGEgtRZUzLLN7HpQtoz9I0BJvTnPrmP6scHtaNr61fmkEDFG6lcdjhhgBPnhqULtaZtM2ph8HTC2KiZluG8h96l5ejXmLfwO0Yt5uzdXVuFGY7/btFWLBSU1lH5v10gkCTq/cXOEVywJ+dTXimJ7f7zYttSUMt37PGGfzRAvMFZXDKOBT6ZAyOiHEMNj6o/HALR81njct03lcW8O3zhasdZdjvx4XcDVSt9DPMoXCynmfSgR/OsW8ETQoTI4iyjm+t37Oj67H+Mw6JsF6c6EfoLqyj1ct84lwtPY8FE3Xm29LpPdRELevd9sZrvvp6+iMC42frENoqG1jkrMJHznb+rQWpyY7MXxgmcRCdw2WuKvHW6vzsWUCbWODreGOFxJUTqWGrNwwhkgE01RdS1HlHD+4/A87KK8cpb32Fy9altA1phfz3XWMvA6e4MJTmPHIzjW2hHjmn3jf0Z6/XLWNwMXs4ejhSbqf2HCwN6Qdbl2hbPREFMw9rHTycZmz+A5hKK2cYJHd09oTP18lRS1lN7/ZPV35q0WNoaX2H7Nd9X0qD5KqMOcoopxnje6/IvRRdR1PaKvp7+jo95lgxUkDdTsDLRMZ6GzrJ08lFPha8aSnoHLBby8sr3Cu01xbzXxXbS7jvxuxnRgaqNtZ5a7ITT8VOMHS4c9s23tUUQ8Z24dzja0hcXOZmI9RXDltVNiUifrJuJ5rKnvoY/mNj5zt2Kf7VjapuLHgMirdQohmd4inhfAdRyeKKmfZ7y5s9LBRcKOjUlE5zIfWnxjmeNEnr3u/hz0hrwCeeXiuEca+2LHL5jzu/S72uQtTQLnICve9xlCoNtpCnyD2N1dD3nJ08fn+ykeNJwo7ebnMupC4HoH3R39JAS6yUS+LG5WXtb99Kib9meuqzYPqFgY62/JrbIWJgps66m4qKYdprq2mt6M7S+0Jh2q1iP6YzXoZ47sJpIhyjuX2Xj7LonQru/TijHQ+wz53EU7g2wpZW9nFr/ZBQNLyy+GhSRsqNf/XMTTb+XaStp3tqkdPx+vk5yJrQjzDtP51VWO867HYBiTT+f2UAR+JHsZCe8JztYp5j7LKcS7q2fjTXZ966g4joPUGeMFYcRp5y+uAu2DAeR7i3/vM98VHoz+lrrqT1toivnW28KlkTo5wrrM1pBMAr8T0ZZu7FGvs3VnprkRbR38+sow3KhcrRY1jim0w/7qqM9L1HFacNFXXscpdkee0pbxr9cyeP8jRJraRJSEVt0+j1Hb7qz49ZgG2uEvxdMwnRh6toBxhvj1uOODvrgd40xH/meQ/5mmqrqOTZS69Hd05peciJ9c4T7jPtmFEsdbezUjHendZz/DejzsQYvUfc2QUqRmvSmCeSkb/d4Ch83cH3zCDsxPDM9pyFruqGYWnSsoh5trfw6UrlI5OvItVONd4UlvNXFedgAWR+J5SV/KV7ZsELXRenzue5ytX4HH7qS0bN9gW4uny+kz0R2yK19oPMMjyI0WVc7zq6Gvc3BTcFFPO8rK2kEmuRziiFzAeONG6lXLRE5OdlqzcoKRymm1JeNjcTfJxibPk4HZWao20fsPT2kogaYUZgCLKWZbbewdc/76jfcAeHanBX4EmWNo7a3/S3zqVQY7WjAsQRHqVVY5xv7qdn1yPGAV2OzFUUg6zSS+TaIEyreXjEr/bBjLV9aDRdfpOkJxgv5Rykpu63ZjP40vrN7TQVvK9szmvWeYmqwIUPBVjCnCepE/M1tvyG2f0nEyNbc1OjsKcY0WIJ6hIztjxjOKAvTWaonNcz8P90f7fanE7PKRuZJR1JP0dHZjhDj6cZZDlR162/INTVykT7WktTiy4f1ZdygWy8587ULf/4Fpr/2DBZbSsp0RLbQlWXEyJzWuvavMJJZpvXU/jfRb0t0ymonKE9o63YyuXNGOduYLghJ6b/8W8zzE9v/HZj7jz0SjmS+N85mCoXNQEnwplDRcN1a1scpehjfYP7Sx/82zMQB5UN3NBz04O5RpTXQ+hoAdsqADfCoeJzkdoZ1nIYMdLjHU9keTvpaG6hYt6NrbrJbHhDNgLyayFupx9euEkDWVIamC+4JfvaLorafO0lI6aZHwv3u+/afRQ9piGgRjnj10/yulp0QVPHs3CTU8wF9tK661U8aXTWlvEXncRv40ogbys/U0B5SJLXVV5WlvBKNeTzLO9S7bYivZeMd04oBfiJvYEFZ8llVMstnuGL9aMGmUM7bxVOYkkCptRQRdCNNFY0VHpY/nNaK1Oi3vpc+pSRthG85WzBToqvSwzElQWA1RSDnOFMIoq5/z2jLxVFpxUVQ7gQjN61Rwa8jhKBp+aPTXjVRljnkq6NCrNvJ4P8MA9eXi0Yn6smicTlcyTxdimdN64v/s1K0/TSvnp06QsUzrFtTw2qZCf2iVz8XxNTw11z4fK8Ef3BjxdrRA1iuVgfPv7sFt8f7ZSpuMCjGvn2zr35YvVCLN5bpAd7vfcpB8qn4+17z7Mi7WKMvJ/1ahUyJORKhbLR/FHujG973M8U91zM8p9T22ejv6YOtHfsXHAIxwe2pyvW1VHUxN+xl5P1Kbooz0Y1/VRnzS89WhZXq5bnFrFPS0yD5ePm8xhtrs+VR3jmeSK22eD2xMML6EmX7me5bWGiQemM7vVZ827nod5jwfjJi17pX4JAF6oVYQnqxbi/eZxrS33lYhrHbJbVOP3sWfJYSyvfa/vTclrUu6enH1qMjmzxLVwDHyqMkf0AnzifJkbWT1jMV+L6cONkAK0ielPq9rFyJM17sH/xYtVeffx8rz5SFk63F+S4c/71iK3rVecPLnzGEH5qw1Kkj007oH8Qq0i2DTV+IzeZfHVK5Vwkr56pXLz0VMJb6q5s9h44B5Pl/1Qq8aiNxuxtO+DjH/FM+Zu0NOVGNeuFs/XLMID9+QhW4iFEGvC20iDMp5z5snq+xCtWyoXz9bwfcg9WC6utv/+Mp5ze1ruFBqVzUvTSp6Wde9nb1LB8+/G5fKyoHdDn+8UoHWduAd/xDOVee/xCkb+B4zP5/XBE55xUdvdJQCIVLJRu2Qu8mS188A9eejbtBxVi+bwyTtex/V89Inpyisxb/Oz05P/3gobZKxf4b6XLDaNfYObcXhoc4Y/X4UQq8qStxrzVau4Frt/3mjEU1ULUSxXGN+8VJ0J7ePGOJYvkLCCq0sjz+zQv7k8hfRdYZ7hDzvdxY3vaNDTnt+3QHgIByIe5583PN37x7ieoE7UN+R+xLcLYIHwhK11e/Wi/OhqhhMLY172nCMaGxv1sgmC8pY1i5AzzLfA2DnedWuJvWfUKJbDuBckplXtotQpGde6msWmGd/HWXIypsZMn6C8dZ1iNKngf6KYIjlDaV6lII9VKmDc2+Kb0rEO07vU49GK+alaJDsj/1fNSLP3fmeWxabx1qOBJ+z6uUOdBPtN6eFbUWPTEl4/LaoV4qU6xTioF/KZZLOPoxtVor4nwvkSz+nDeMXxdoLru1TeLLSsGXcf6NywFHNe97Rc5sxXOGBQHh5ioXcTz333ty71jGfFt3pLLldozUt1Ehaozfo2jbtXPlfDc/4T5KVtzDs8Ef0JVYokPG/5AtkomScL2UOtvFy3OKri+Q286SiZJwvhIRYalMmd4DnXpVFpej0cV2n64ZO+402996Dasfnn8coFfNZXLOh55mW1W4w01y3l25Lfx9ENp2LlU8f/AOj58D0+91t/ahTL4Xe5onju4e0bxO0/r6fvuOiiuUJZ+25cBciIllXp/mBp/nXXoFL0uARBufnZazbM+T9GOJ6nb/6xxrLPHZ5Z5s1DUv7q/QAFwkOY4W6YrKC8WK6EFUuTXU0CBuU1iuWgcuG43//Rivl5tnphVvV/iMbl8hr39umuxuwv2tLY7kdXM751teC9x+N+2yHO1iysNYZG5QvF9viIu74K583FW2X/ouc9f9Mg+mujV5Z3Qsn4rZmRZOGB6C9oFP15gl5ebRuUZrG7OpfJxrfuZ3hUHcsxPT8/uZoy112XG1Xa0axyYVrUKE7vJvcY94n4orExIUcPvgl5jR+ydeX+6JGMja0Q9eY3u0WlfuncRDxTmSYV8qEqUKt4ToY86+mNt9RdlTNZK0C8oUG9Hr6HMJtGNruF+Kef5b6f11s/z7DnfMsV8b3aIOlzEKh60l6L1inmDZ/KivfyfE3nmD5GUF49wDUywdmUbwtF8FD0Z4BneNRPzrhyYRR243nwWKUC/N2nIYvebMy+oi8kCMq7NfZ9g0L90rlpVTvuHjbJ9SjDnf9jjV6Bd50dOabn58WYAcxx1aVx9Aj+cN/PmawVeLpJwqEjZ/ScROqhnNZzcimJDVAA9+RL/HWblwg3gvISucOIwm48Z793NudvV00GaJ7eFy/XLc5jlXzvaZqq8HefhhTJ6ekx9FyNIpQvkC1BGcmf390NuS/qOz53tuShLl9ST52SICgHTw/F43o+v0H5Zy197yFFc4X6fOdJUe+eAvTv/IoRlD9ZtVCGD8pTm7SYi4zl5GY4sgLqdIFzeyB3GbAEv6mkusPL4fo5z2tCRObgdsP6cVC0DhRMvDATcP/r5yBbfrhwAK6fh2IJJ4FMVZePwZ55UL0NuF1gDQNNpg4RItXoOnfFi3Tdbji7A/JWyHz3iGvn4NB/UOGp9ClPpCJd11MlUPn0r92MWnKAVxuU5IN4lVqBLJz2FY/sTvhGDbOhjv8x2vUUPR8qwxuP+m/YSODGRYi6ArkCVBKc3eV5tuUs7n99erh2DqwhYE96YC7uXqkZr2ayu7PI8ApV8/wHkD9pD4s0USLh7NPiLqeqULvTre2fLXbcfO7Snv/SWo6iUCd5M3gLIZLhbgjKwXN/KpD6r6e6I2TNC5WfD77dHSC1Wg+9R9GTMaHbwTwPJjoWG0xvI0hOOsNyef4LJF/yJ9hNc1n9zy4uxK2SwFwIIYQQQohMwhs3J6fPrFML5eGYEYDOi9oSell+p5By0XebRMbbCyGCk8BcCCGEEEKITEK5pYlVFX5xPcgvrsbYcdBcXc3nttGA7+t0hRDJJ4G5EEIIIYQQmURci/mtTDOlEI2NGe6GWB0uqioH+CeDvNZViDuVBOZCCCGEEEJkEnFjzFPHL64H+YW4GczvkpkZhLjt5HVpQgghhBBCZBaxTeYZ971MQmROEpgLIYQQQgiRSaSkRftueUGBEBmZBOZCCCGEEEJkMsl5XVpySBAvRMpIYC6EEEIIIUQmkZLXpQkh0p4E5kIIIYQQQmQS3telSVwuRMYigbkQQgghhBCZRFq3mN/ae9KFyLwkMBdCCCGEECKTiAubpc1ciIxEAnMhhBBCCCEyCRljLkTGJIG5EEIIIYQQmYSSgveYS/d0IdKeBOZCCCGEEEJkMvK6NCEyFgnMhRBCCCGEyCSkK7sQGVOaBeaDBw+mfv36hIWFkSNHjrQ6jRBCCCGEECKJ5HVpQmRMaRaYx8TE0LJlS7p27ZpWpxBCCCGEEEIkQ9q/Lk0IkRKWtDrwRx99BMCECRPS6hRCCCGEEEKIFPh943GeqV44SdvuO3M1yceNcblTmiQhMrU0C8xTIjo6mujoaOPfkZGR6ZgaIYQQQggh7i5ztp40/m4zbk2qH/+nVUd489FyqX5cIe52GSowHzJkiNHSLoQQQgghhEhd3RqXodvkjQCUL5AtSfs4XG4OnLuepG2HPV8lxWkTIjNTdD3pI0z69evHp59+mug2u3btonz58sa/J0yYQO/evbl8+XLQ4/trMS9atChXrlwhPDw8qckUQgghhBBCBOB266hq8kaDRztdKCjcdLjIZve07emAW9exaioxTjc2i7zwSWQukZGRZM+ePVXi1WS1mL/55pu88soriW5TqlSpFCfGbrdjt9tTvL8QQgghhBAicckNygHsFg0gQfCtxU73JkG5ELcmWYF53rx5yZs3b1qlJQFvY76MNRdCCCGEEEIIkZF449RkdEIPKM3GmB89epSLFy9y9OhRXC4XmzdvBqBMmTJkzZo1Sce4etUzA2TRokXTKplCCCGEEEIIIUSKXb16lezZs9/SMZI1xjw5XnnlFSZOnJhg+eLFi2ncuHGSjuF2uzl58iTZsmVDUTL2WxG94+GPHTsm4+FFhiX5VNwJJJ+KO4HkU3EnkHwq7gR3cj7VdZ2rV69SqFAhVPXWhnOkWWCe2aTmwH8h0orkU3EnkHwq7gSST8WdQPKpuBNIPvWQWRqEEEIIIYQQQoh0JIG5EEIIIYQQQgiRjiQwTyV2u50PP/xQXvcmMjTJp+JOIPlU3Akkn4o7geRTcSeQfOohY8yFEEIIIYQQQoh0JC3mQgghhBBCCCFEOpLAXAghhBBCCCGESEcSmAshhBBCCCGEEOlIAnMhhBBCCCGEECIdSWCeCr799ltKlChBSEgIderUYe3atemdJHGXGjJkCPfddx/ZsmUjX758tGjRgj179vhsExUVRffu3cmdOzdZs2blueee48yZMz7bHD16lObNmxMWFka+fPno27cvTqfTZ5slS5ZQo0YN7HY7ZcqUYcKECWn98cRdaujQoSiKQu/evY1lkk9FRnHixAnatGlD7ty5CQ0NpXLlyqxfv95Yr+s6H3zwAQULFiQ0NJQmTZqwb98+n2NcvHiR1q1bEx4eTo4cOejQoQPXrl3z2Wbr1q088MADhISEULRoUYYNG3ZbPp+487lcLgYMGEDJkiUJDQ2ldOnSDBo0CPP8zZJPxe22dOlSnnzySQoVKoSiKMyaNctn/e3Mk9OnT6d8+fKEhIRQuXJl5s2bl+qf97bQxS2ZNm2abrPZ9B9//FHfsWOH3qlTJz1Hjhz6mTNn0jtp4i7UtGlTffz48fr27dv1zZs3648//rherFgx/dq1a8Y2Xbp00YsWLaovWrRIX79+vV63bl29fv36xnqn06nfe++9epMmTfRNmzbp8+bN0/PkyaP379/f2ObgwYN6WFiY/sYbb+g7d+7Uv/76a13TNP2vv/66rZ9X3PnWrl2rlyhRQq9SpYreq1cvY7nkU5ERXLx4US9evLj+yiuv6GvWrNEPHjyoL1iwQN+/f7+xzdChQ/Xs2bPrs2bN0rds2aI/9dRTesmSJfWbN28a2zz22GN61apV9dWrV+vLli3Ty5Qpo7dq1cpYf+XKFT1//vx669at9e3bt+tTp07VQ0ND9TFjxtzWzyvuTIMHD9Zz586tz5kzRz906JA+ffp0PWvWrPrIkSONbSSfittt3rx5+nvvvafPmDFDB/SZM2f6rL9deXLFihW6pmn6sGHD9J07d+rvv/++brVa9W3btqX5d5DaJDC/RbVr19a7d+9u/NvlcumFChXShwwZko6pEpnF2bNndUD/77//dF3X9cuXL+tWq1WfPn26sc2uXbt0QF+1apWu654bqaqq+unTp41tRo0apYeHh+vR0dG6ruv622+/rVeqVMnnXC+++KLetGnTtP5I4i5y9epV/Z577tEXLlyoN2rUyAjMJZ+KjOKdd97R77///oDr3W63XqBAAX348OHGssuXL+t2u12fOnWqruu6vnPnTh3Q161bZ2wzf/58XVEU/cSJE7qu6/p3332n58yZ08i73nOXK1cutT+SuAs1b95cf/XVV32WPfvss3rr1q11XZd8KtJf/MD8dubJF154QW/evLlPeurUqaN37tw5VT/j7SBd2W9BTEwMGzZsoEmTJsYyVVVp0qQJq1atSseUicziypUrAOTKlQuADRs24HA4fPJk+fLlKVasmJEnV61aReXKlcmfP7+xTdOmTYmMjGTHjh3GNuZjeLeRfC2So3v37jRv3jxBXpJ8KjKK2bNnU6tWLVq2bEm+fPmoXr06Y8eONdYfOnSI06dP++Sz7NmzU6dOHZ+8miNHDmrVqmVs06RJE1RVZc2aNcY2DRs2xGazGds0bdqUPXv2cOnSpbT+mOIOV79+fRYtWsTevXsB2LJlC8uXL6dZs2aA5FOR8dzOPHk3lQUkML8F58+fx+Vy+RQcAfLnz8/p06fTKVUis3C73fTu3ZsGDRpw7733AnD69GlsNhs5cuTw2dacJ0+fPu03z3rXJbZNZGQkN2/eTIuPI+4y06ZNY+PGjQwZMiTBOsmnIqM4ePAgo0aN4p577mHBggV07dqVnj17MnHiRCAuryX2nD99+jT58uXzWW+xWMiVK1ey8rMQgfTr14///e9/lC9fHqvVSvXq1enduzetW7cGJJ+KjOd25slA29yJedaS3gkQQqRM9+7d2b59O8uXL0/vpAjh49ixY/Tq1YuFCxcSEhKS3skRIiC3202tWrWIiIgAoHr16mzfvp3Ro0fTrl27dE6dEB6//vorkydPZsqUKVSqVInNmzfTu3dvChUqJPlUiLuItJjfgjx58qBpWoKZhM+cOUOBAgXSKVUiM+jRowdz5sxh8eLFFClSxFheoEABYmJiuHz5ss/25jxZoEABv3nWuy6xbcLDwwkNDU3tjyPuMhs2bODs2bPUqFEDi8WCxWLhv//+46uvvsJisZA/f37JpyJDKFiwIBUrVvRZVqFCBY4ePQrE5bXEnvMFChTg7NmzPuudTicXL15MVn4WIpC+ffsareaVK1fm5Zdfpk+fPkaPJMmnIqO5nXky0DZ3Yp6VwPwW2Gw2atasyaJFi4xlbrebRYsWUa9evXRMmbhb6bpOjx49mDlzJv/++y8lS5b0WV+zZk2sVqtPntyzZw9Hjx418mS9evXYtm2bz81w4cKFhIeHGwXUevXq+RzDu43ka5EUDz/8MNu2bWPz5s3Gf7Vq1aJ169bG35JPRUbQoEGDBK+c3Lt3L8WLFwegZMmSFChQwCefRUZGsmbNGp+8evnyZTZs2GBs8++//+J2u6lTp46xzdKlS3E4HMY2CxcupFy5cuTMmTPNPp+4O9y4cQNV9S2ya5qG2+0GJJ+KjOd25sm7qiyQ3rPP3emmTZum2+12fcKECfrOnTv11157Tc+RI4fPTMJCpJauXbvq2bNn15csWaKfOnXK+O/GjRvGNl26dNGLFSum//vvv/r69ev1evXq6fXq1TPWe19D9eijj+qbN2/W//rrLz1v3rx+X0PVt29ffdeuXfq3334rr6ESt8Q8K7uuSz4VGcPatWt1i8WiDx48WN+3b58+efJkPSwsTP/555+NbYYOHarnyJFD/+OPP/StW7fqTz/9tN9X/lSvXl1fs2aNvnz5cv2ee+7xeeXP5cuX9fz58+svv/yyvn37dn3atGl6WFiYvIZKJEm7du30woULG69LmzFjhp4nTx797bffNraRfCput6tXr+qbNm3SN23apAP6559/rm/atEk/cuSIruu3L0+uWLFCt1gs+meffabv2rVL//DDD+V1aZnZ119/rRcrVky32Wx67dq19dWrV6d3ksRdCvD73/jx441tbt68qXfr1k3PmTOnHhYWpj/zzDP6qVOnfI5z+PBhvVmzZnpoaKieJ08e/c0339QdDofPNosXL9arVaum22w2vVSpUj7nECK54gfmkk9FRvHnn3/q9957r2632/Xy5cvr33//vc96t9utDxgwQM+fP79ut9v1hx9+WN+zZ4/PNhcuXNBbtWqlZ82aVQ8PD9fbt2+vX7161WebLVu26Pfff79ut9v1woUL60OHDk3zzybuDpGRkXqvXr30YsWK6SEhIXqpUqX09957z+cVUpJPxe22ePFiv2XSdu3a6bp+e/Pkr7/+qpctW1a32Wx6pUqV9Llz56bZ505Liq7revq01QshhBBCCCGEEELGmAshhBBCCCGEEOlIAnMhhBBCCCGEECIdSWAuhBBCCCGEEEKkIwnMhRBCCCGEEEKIdCSBuRBCCCGEEEIIkY4kMBdCCCGEEEIIIdKRBOZCCCGEEEIIIUQ6ksBcCCGEEEIIIYRIRxKYCyGEEEIIIYQQ6UgCcyGEEEIIIYQQIh1JYC6EEEIIIYQQQqQjCcyFEEIIIYQQQoh0JIG5EEIIIYQQQgiRjiQwF0IIIYQQQggh0pEE5kIIIYQQQgghRDqSwFwIIYQQQgghhEhHEpgLIYQQQgghhBDpSAJzIYQQQgghhBAiHUlgLoQQQgghhBBCpCMJzIUQQgghhBBCiHQkgbkQQgiRCVy/fj29kyCEEEKIACQwF0IIIe4yAwcORFEUdu7cyUsvvUTOnDkpUqQIiqKwadOmBNtHRESgaRonTpxIh9QKIYQQQgJzIYQQ4i7VsmVLbty4QUREBAMHDiQ0NJTJkycn2G7y5Mk0btyYwoULp0MqhRBCCGFJ7wQIIYQQIm1UrVqVKVOmGP9es2YNU6dOZdiwYaiqp25+06ZN7Ny5k759+6ZXMoUQQohMT1rMhRBCiLtUly5dfP7dtm1bTp48yeLFi41lkydPJjQ0lOeee+52J08IIYQQsSQwF0IIIe5SJUuW9Pn3I488QsGCBY3u7G63m6lTp/L000+TLVu29EiiEEIIIZDAXAghhLhrhYaG+vxb0zReeuklfv/9d6Kioli8eDEnT56kTZs26ZRCIYQQQoAE5kIIIUSm0rZtWyIjI/nzzz+ZPHkyefPmpWnTpumdLCGEECJTk8nfhBBCiEykSpUqVKlShR9++IHVq1fTrl07LBYpDgghhBDpSVrMhRBCiEymbdu2/P3330RGRko3diGEECIDkMBcCCGEyGRat26NpmmULVuW2rVrp3dyhBBCiExPAnMhhBDiLjNw4EB0XSdPnjx+11ssFhRFkdZyIYQQIoOQwFwIIYTIZCZMmIDL5eLll19O76QIIYQQApn8TQghhMg0/v33X3bu3MngwYNp0aIFJUqUSO8kCSGEEAJQdF3X0zsRQgghhEh7jRs3ZuXKlTRo0ICff/6ZwoULp3eShBBCCIEE5kIIIYQQQgghRLqSMeZCCCGEEEIIIUQ6ksBcCCGEEEIIIYRIR2k6+dvSpUsZPnw4GzZs4NSpU8ycOZMWLVokeX+3283JkyfJli0biqKkXUKFEEIIIYQQQohk0HWdq1evUqhQIVT11tq80zQwv379OlWrVuXVV1/l2WefTfb+J0+epGjRommQMiGEEEIIIYQQ4tYdO3aMIkWK3NIx0jQwb9asGc2aNUvx/tmyZQM8HzQ8PDy1kiWEEEIIIYQQQtySyMhIihYtasSttyJDjzH3dl8PDw/P8P+5tBBaT9zKrvMOGny+iv5/7ke3hFIlYhkT1p1h+7kYqkQsY+BfB2k9cSuLD15l9wUnVSKWcfCKmx9Wn6JKxDIeH7WeF37czN/7Ipmw7gxVIpahW0KZu/sy/f7cx5mbCi9N3ML5aJU/dl6i1YQtVIlYRu3hK6g1bAUO1U77ydvZdjbGOGaViGU0+249fWbsIdJloc1PWzl6Vaf/nP3U/HQ5HafsoMnXa6kSsczYf8g/h3lr1l7enbOf37ZeoErEMj5ddITtZz2f4/Mlx2j23XoW7L1CFDaqRCzjj52XOHZN58GRa/hh9Sm6/LKThfsi2XfJRZWIZey75GTRgUiqRCzjg3kHaPbdek7dUNh8OpoOU7aj2sOYu/syb87aS6TLQqsJWzh9U+HXLeeNtEUsPEyViGWsOHKdKhHLOH1Toem367hv2ApajN1IlYhljF55kt0XnLz801au61aqRCyj3mcrje/C+98DX6zm9E2FVhO20P/P/cZ3cMmh0ev33Sw+eJWVR6/T7dddLD54lQ5TthvpqD9iFe/O2c8fOy9RJWIZUdjYdDqajlN2gC2U/nP288eOi2w9E0P7ydtRbKG8NHGLsf/nS44xZtVJDl1x8/JPW4lW7AxddIQqEcuwhmThndn7GLroCN2n76LPjD1YQ7JQ97OVDJh3gLAsWen7x17m7b7MV0uPUyViGdfcns85fu0ZzkWrtJ64lUsOjYF/HWTqpnOsPHrd+Nw1hi6nSsQyjl7VGbn0OPU+W8nz4zZRJWIZG05GGduej1b5YN4Bn+/sbJTCyqM3eG3aDqwhWej5+27+2R/JdytOMnLpcVR7GN2n72LVsRv8seMiVSKWUWvYCqpELGPqpnMcidSpErGMHeccLNwXSbUhy7gvdv3WMzGsPHoj9jwqLcdt5qGv1tLvz31UiVjGsWs6rSZs4YUfN5MtWzYGLTjEzxvPsurYDbr+uhOnFkKViGW0+Wkriw5E0uDzVTT6cjVD/jlCt193YQkJ44N5B2jz01ba/byNKhHLGLv6FNvPen6jc9EqVSKWMWPbBaIVOy//tJWDV9y8Nm0Hj49a73O9/rnL87tHuiz0mbGHhfsiWXr4Gt1+3UVolqx8vOAQtT5dwRWnhSoRy/h92wWG/HOEakOWgS2Uh75aQ58Ze1BsocY1XCViGf3/3E9IWFbqDF/J+3MPcD42Td7/Bv51kFM3FFpP3Eqky8Lm09G88vM2dGso3X7dxdLD19hwKooqEcs4dUNhxjbPtXshRqNKxDImbTjL7Ng8O2nDWXr9vpsqEcuYsukcTb5ey/qTN5m66RxVIpbx3A+beCX2e3JpIbwx05PnFu7zXMNXnBaG/HPEyNMdpmyn9vAV3P/5KiMfd5yygyfHbCA0S1b6zNhDtSGee9zbf+wjLEtW4ze85NCMz1htyDImrjuDYvPcP8esOsnFGI2XJm7h6FVP/vl2+Qnj721nY1h/8qaRpztP28nKo9eNvLb9XAyHI9088MVq4xzz91zm6FWdNj9t5SY2lh6+RpWIZdT9bCU1hi5n9bEbbDgZxauTt2MLzcL02HvgU2M2UG3IMhbsvcLM7ReNe+ugBYd4Y+YeXLF5cPzaM/SM/W6rRCzjwZFrGLfmNPsuuWjz01aiFBudp+1kxZHrXHZ48sh/h67x9bLjPr/35tPRzNl1mSoRy7iJjV+3nOeDeQe44rTQeuJWztxUfO5BUzed4+MFh9gYmweOX4N3Zu/jjx0X0exhdJy6gw0no5i/5zJvzdqLNSQLD45cwxsz96Law6gSsYyvlh43nh0O1U6jL1fz1qy92EKz8Ppvu1ly6BrLj1yn+/RdxKh2qkQso/v0XbQct9m4t289E8Ork7dzExs9ftvN5I3njM+27sRNvl1xgioRy4xr7u99kcZvePqmwuKDV+n5+26cWgizd14iWrGz/uRNOkzx/B4fLzjE5I3n2HgqivaTt2MN8aTt4a/XGnmt+/RdTFx3xvgNO0zZztPfb8QakoWqQ+Ly2juz9xGaJSvNvltPl192kiVrNp/frsOU7ca9pcX3G2k/eTv1R6zijZl7jGvphm7jw/kHeXLMBmO/satPsfO8g5cnbTOeQ/VHeK6N58dtYuXRG0YeOnTFTZWIZXyz/ITx3aw9fpPpWy8wYN4BDl1xG/lm8cGrxj2yzvCVPPTVGt6YucfIi8evYdx3/tkfSedpO2n23XpcWghtftrKvktO9l703Ms+XnCI13/bzS+bzxvHvea28vGCQ9QYupwoxfNs/2XzeXr+vpuHvlpLlqzZjGPejH32z9pxkfFrzzB00RGyZctGm5+28vy4TThjz7nzvINaw1Yw8K+DRrlo/Nozxue6EKPx8qRt7DzvYNKGs8Y9548dF+k/Zz8XYzRaT9zK2SiFgX8dZNrmc2ix+fXL/44za8dF497pzc/ee+eKI9eZt/syff/Yyw3dk97ftl4w8vs3y0/w/SpPfvfeM5Ydvm5sO2fXZf7ZH0nP33ejW0J5edI2dpxzcPIGxnNrwLwD1P1sJetO3DSeVe/O2U/t4SsIzZKVN2d57p3e+7U1JIvxzHVpITQe6Xke/Lwx7rN3nLqDJ0ZvwBqShU5Td7D62A3m7r7M23/swxqShVrDVvDh/IPGNTh96wVeHL+FF8dvMX7/RQciPWn+aSt7LjqZuO4MQ/45YqxffPAqG07GPS++XXGCkUuPs/l0tHH/SG5Z+Pg1fK4ff/91+3UXI5cep8svO32eh8Y96lQUM7d7fvuQsKw0HrmGN2ftNfLbHzsv8cPqU4xYfNT4Hb9bcdLnXrrogOc38z5b4n8XEQsP89P6M2w9E2P8ZtvPxvDKz9twqHYafrmavn/s9SmHT4z9O0qx0ev33fx74Corj3rKH9aQLIxaedK4b9YYupyPFxwy7hFR2Bj892GqDlmGLTQLny46wvi1Z1h0IJLXf9uNNSQLD3yxmndm70O3eq6RH9eeZvTKk3y+5FhcOXvHRZ+898vm83w4/6Dx3fy9L5KrbqtRfh6x+ChjV5/yeR6uPR6XT73xirdMcxOb8cy55rby7pz9zNx+kb/2XKFKxDKuuq18s9xzj1LtYYxcepxRK08a97IoxcZHfx2iSoSnLN3vz33M3nmJtcdv0nHKDmyhWYwyhrdc8tP6M7w39wD1PluJNSQLT47ZQIcp29M9rkvuf+a49ZZi39v1ujRFUYKOMY+OjiY6Otr4t7cG4sqVKxm+xbxEv7npnYQ7Xu4sNi5cj0nvZGR4z1QvzMxNJ9I7GX5ltVu4Fu1M8/M8VbUQs7ecTPPzpETN4jnZcORSeicjQ1EUiP+kuVO+p5J5snDo/PX0TsYtK5YrjKMXb6R4//IFsrH79NVUTFHGObc5f5bNn5W9Z66l2bnudOXyZ2PPmcC/Res6xZi85miap8NmUYlxun2W+bvPBBJm07gR40qDlN1dDg9tnqztU7ssnJzfND1VK5qDzccup3cyMrxKhcLZcTIySdtO6ViH+mXypHGKUkdkZCTZs2dPlXg1Q7WYDxkyhOzZsxv/yfjyzEWC8qTJqEE5cFuCciDDBuXAHRFs3m7+ClZ3yvd0NwTlwC0F5UC6BeW349zm/ClBeeISC8qB2xKUAwmCckheACdB+Z3hTgjKAQnKkyipQTnAzlNJ3/ZukqEC8/79+3PlyhXjv2PHjqV3koQQQgghhBBCiDSVppO/JZfdbsdut6d3MoQQQgghhBBCiNsmTQPza9eusX//fuPfhw4dYvPmzeTKlYtixYql5amFEEIIIYQQQog7QpoG5uvXr+fBBx80/v3GG28A0K5dOyZMmJCWpxZCCCGEEEIIIe4IaRqYN27cmNs06bsQQgghhBBCCHFHylCTvwkhhBBCCCGEEJmNBOZCCCGEEEIIIUQ6ksBcCCGEEEIIIUSGkFlHQktgLoQQQgghhBBCpCMJzIUQQgghhBBCZAiKkt4pSB8SmIsMr4hyjmbqGhTc6Z0UIYQQQgi/QohO7yQI4YdOOeUoqpSjMzwJzEVACu4MEQwvt/dilG0kT6sr0zspIqA7fzCQgpvayi6yceOWj2XDQXaupUKqhBBC3Alaa/+wO6Q9z6lL0/Q8Cm7etUymubo6Tc8j7h7dtD9YYO/HUMvY9E4KTdQNDLOMwU5MeiclQ5LAPI1puNI7CSmi4Gau7T3+tL1PegRd+bjEN9avqK3sMpbVVXfe9nSkJjsxFFdOp3cyUp2Gi9m29/nW+mV6J+WWtNT+41f7IH63feiz/CF1I4+pa32WabgoyIWAx1pm78WWkNfIRWSapDU95eUyz2v/ZYiHamnlRKZsoQrnOh9aJlJV2Z/eSREizXTRZtNdm4UVJxWUI6S0LHKPcpwO2jwsOFM3gbFU3NiJYbD1RwBG2Eb7rM/DFbKmQoWvVxN1I69Z5vKt7asUHkGngnIkQ9zD04qCG2sa/d5pTcHNI+p68nMx1Y7ZyzIDgBcs/6XaMVPqB9sIXrD8R0dtXnonJUOSwDwNlVBOscP+Kv0tk9M7KcmWlytUVI9wr3qYvFzmC+u3PJviWmCd0sqJgK3vddWd/GL7mLLKMWNZhPUHntBW86t9UArPmTgLTsKI8rtOxc0U6ycMtoxLpbN5ChMzbR/yn/0Nn8qGYHJzhXrqDjJyi3RV5QBV1EM019YG3zhFdIopZ0ir76CEcopH1XU8Fdsjo6x6wlhnwcmPts8YbfuSHFw1lk+0DmVVyOs0ULcZyxTcRiCeX7kMwH3qnjRJs1dOInlSXUkoUfxoHUYPbWaang9gpv0DPrOO4S3Lr2l6HhU3r2h/xRbIE6qn7mCRvS9zbe+maToyHp1+lim0tyzgD/sHAbfKCL2dMpLayq5kV+5acVJeOUr63X9Tdt6CXGC09QvqKLt4VZvP+5ZJKT5WUoURlaqBUAjR9LNOo6/1V6bZBjHf3p922t/UU3fwkrYoWcdaaH+bAdafkxUI5OMSY60juN90jw9krq0/u+zt/a7Lxg3Wh3Rle0hHn+WllJO00halqPEmr3Il2fuYPasuY769Pz/Zht7ScZLK0306KflPp6xyLMF3kp1rhAYorwXyi20Qm+2dyMLNZO2XVHm5lKB3XR6u8IC6lVu91p5WVzLW9jkr7a8bywpygWm2QTRV06qclXSllJM8rG5I1j7ZuEEzdY1PZVABxX/FQyttES21JbeQwjubBOZpqLfld0IUB50tc/2ub6RuYZHtTWooe29zypLneW0Zz2gr+DxeLbCXnRj6WqZRLUDrTS9tBovsffnAMsnv+mm2T6ij7uYn21AqKYdor82nhHImwXY6qTcTxH/2PuwMedVvLXZNZS/1tZ20tiTv4Z+bK1RSDvssa66uZpO9M/XUHVRUPQHG81rSKzj+tb/JVNtgHlPXmZbqlFJOprjgXV3Zx3xbv9iA3yOUKF7R/qIw57ATQ21lV5ILDGk9ZqmfZRpL7X3ooc1igGUSQy3fp+rxl9jf5HvbF9yvxX0fpZUTDLGMpbgpH2ZT4vKKd9u22kJj2WDLODaGdPH5XtOmMKxTSTmMDQdTbBF8bfuGv2z9eEjbzFvW6UH3tuLkPmV3iluPiijnAU93tKSkNaW9hl7UFjPQ+hPz7f39rn9aXQFAafVUio6f3hL7Xoorpxlv/ZRaym6f5R9aJrLc3oua6r5Ej/2YupZt9o40VjclISV6uo07LKGcYpHtTZ7XfFtxwrmWaI+U5LLh4Ff7IKbZPklWy2WE5Qf+svejpZY2rUz11B2MsH7nU+kXR+cX2yAmWweT3PvIcOtoHtPW8Yt9EB9YJ9HRMp9H1A1EWH6gjHI8VdJuFkoUO0NeZaW9R6odUzF9Zm9+72SZy1TbYCKs47gv3rXhofOIup4iylm/x0xORekn1h95RNvAz7YhPsv9tTJXUI+hKv5/o1LKSePvxupmplo/obhymn/tbzHEOo7W2j9JTlNqaWPxnLOO6u87TF0WnPxre5PfbQN9ltdU9tBFm42Cm7xciu3VMJ+/7e8w3DrG2C4rN9gS8ho77B1QcdNY3eRzveTgKvNs/emkzfE5fm11D1mUaOr7PI8TqqgcZqx1BPck47oI5xrrQrqzLV5lyxJ7HybZhvKkuspY9rC6gd9sA2MbF+IUUc4GbBx6QPNUBmmmPPWedTJ11V2MsX2ZSMoC3yeUVCyL/Gt/i3G2EdRJRiPTWNsIRtlGBowDvHJwlSHWcQy3fo/qTF5lzN1CAvM0FCyQnGj7lNLqqdtWa5lS2ZXEx8p2tcymu2U2swK03vSx/g5Ae8uCRI9TQLnEXPt7fGidRBn1ZIL1qXljKax4Cn2V1UMJ1mlKXCG1vHKUkdZvktQFfUNIV+ba36WScpj8XCQvl/jW9hU5lWtMtQ02trMrjiSnM3tsMPiItt5Y1l37g3/tb/GBZRKPqWt51zIZOzH00GZSWTnos38R5Vxsi0+cX2wfU0E96pOmdyzTGGj9iXn2/nxrHcmv9kH0svye5HQmJg9XWGzrQzdtVor272L5E4C3rNPpYJnP/yxLKKKcS/L+FZXDzLINiBcwJ26a7RNaWRYzwTos0e3MAc1LlsWedKZxS/LT6grm2t/lS+u3VFA9v21xNX5BNPC18onlR6bbP07wgMzCTb8BmrcVLCUB9iTrEFbaX09yl0k7Mcyyvc+7lskJKrnic9/Bj69KymH22tvSx/Kb3/XfWUfyoLaF3+wfo+KmqrIfK07aWxZQRDlPOdV/IfJZdSmN1U2Mtn1JViWKCbbhtNb+4UPLREAnnOsJfsdx1s9YYe8ZcEhADq4GLEAmRTnlKIU4Tz4u8b5lEiWUuIqUT61jKa2e4jNTQRxga8hrrAp5ndzEtQy+bZnGP7a3UjQHhMX0mXMq/oJg/1paPJWogX6nWzXVNpjntOUMsP6cYF12rlNH3U0DbQcVlSP0sfxGIc4n6biFlYTbjbV9zkuWf5kZb6hOSrTUlvj0zikXG9TkVSIJdO9JjcofzXSMYn6C70fV9Yy1fc5ye2+/+4cRTQdtHvfGe0620hYxxvq5z30qv3LJ+PsZdRlTrJ/wnLqUPSGv0EHz39jizw1CjL8n2IZRT9vJGOsXxrIa6j5KKycoEK8iqqJymIbqFkKJYrbtPfpaplGI89yrHMzA/ecSKqccp4R6JkFl4u/2j+hnncYz6nLWhXQ3ejUAPKstp4hyjmfUZVSILb+oik47bQETbMOZYcrDr1nmUlE9wnvWKX7Pby6HV1YOEmEZ63Nf+d02kEe0DUwxlYcU3Ey0Dg3Ya7KMkrB8CpBV8dwnG2ubjWXjbCOope5lhHWUsay0coLl9t4+LeKBPKBupY/lN5/7noqbJ9RVFCauHJSVG/xre5MB8Z7rj6rraKclXvb20Cka2zNRwW3c84so53zOY1bD9Js+py7lP1tvyijHsRNDReUw5ntBXdUTxAdrmAozPYdU/c4cCnyr7tySTYaUstul92IGTy3ianv3BGNak6OscozwVJx4KlgFQ3JqGu8Ebj3u8860fcDT2kp+tA5P8v6N1c2sCenBupDuftenpKXSZtqnr9UT+LW3LGC07Utes8zlF9vHvGWdzp/29332W27vxV/2fuQlrpBhUxLe7B6I7a6XXblBE83TyvZKkm7mwXW3zKKkeoa3rYED1uS2/ltw0kpblKTWhvG2YVRTD/hURKi4+cQyzui+Hp+3q2BRNfEKANXPNW/Dt+IlL5dStVtxd8sfADweYOjAw+oGNttf48EAraUvWpYA0NYS19qfhyvsCOnALNsAn20HWiawL6QtjdVN7LC/ykDLhGSl9QFtO/mVy9T20zITQjRzbf3pZ4krUDVT11JNPchrlrlBA293KvagCcTTDTL4ff05dSmvazOSfNz3LT+jKTq9LDPIwk0eU9cSQjR5uEI1Zb9PYNXL8jt/2D/gM6v/HkteRZUzfG4bzQSb771qsPVH2lsW8Ki6nq0hnZgTr+v/w9omCioXaaBuT3DMbNxgc0hndoa8muTPZpaXSyyw92NlSE++tn1NR8t8ZtniKnBDg8wP4O1lBNDNMpsy6smA3ZgfV1fzvXWE38DdXKlrJ+kVo17WFPb6eFDdxHzbOz6VTEWUs6ywv+4T3PmraDQ/d+fZ36WXZQZLAwSc8SV27WRTgnftfV77j08t3wesjBtu/Z63rNMpFxs0RWEz1tniPd+ycJNs3GCl/XWGW3zzcOK/WULm4N6uOBhl/YJG6hZjmb/7jFk9bScDrD8zx/4+pZUTTLMNop66gyHWcTTV1vs888zf4Re2UdTXdhpjxwdYgw9P9A5tcvn5LUqaKvrzcoVF9r6sDvEEad4Ksnn2d/nJ9ikdtXlUUQ/R3TKblSE9mWN/32+lRHx5uEJGGAIXrlxPdH0t1X+P0eX2XnxhG0VrS9wzvrm2BoBSatz3F6zCOJRoumqzKa2c4E/7+7xkWcwn1h+po+yigbqNUMVTGZNXuUInbQ6HQ16iibqRRtpWo9ek9/mdn4s8qq7DiWYc34LTT8Vzwtybg+tUUg5TWTlII3WrZ1m87+Z57T8aq5t9lk2yDaWXZQaNtK3Gsue0pXxj+5oVIb2oq+7kNe1PnteWUko9TQfLfApwgW7aH+Qkku9tX/CRdaJPue8+ZXeC4aPttL9ZZu/Dh5afmGobzM6QVymunGa5vRcrQnrRUN3C99YRPhVI5rLOCNtoiqtnGW79nu+tnzPP/i7PqssSfA/mHKmgk5srMlFuPJb0TsDdooG6jW+sX/OuowOPaes4rOdP0XHG24YRrtxktO1LSkT5rwFMzL3KQebY3+eaHsK90T+mKA3x3Y4C8K0IIwoHFhwpyM5uPeFD01wg8t60vd1lbTiIwZroMfMEGf+lAJ9avmevXpgzei66WP6km6MXRxPJM8EKhtXUg4muL6Wc5pyeM5E0Jf0B3kWbjY7CGNeTAbbQMT+YzK1V+blIN8sf/OR6lAN6YcAz9vMH2wgGOtoyw90wSWkII5ohVk9t9hxXXa6QNeC23vHeZk+pK2ljWUQbkj5cwd9VYMPBCOso/nHVMJZZTIXHRupWxti+ZK6rNt0dvY3l+bhEduU6+/QixjJPTbODHXrJRNMRLGAdZxsBwHjb8CTfQx7SNgJQJV4PklcsfwMYwZ733xB3nSi40VMQRD+traSSeoRK6hGGOl8CwKrEFer9FWhV3Ay3jmGj+540bzFvpq5hlG0k/RwdmeZ6KNFtvQX2Re4a7NRLJOs8I63f0ETbxAzX/TyrLU+wvpfF0zL5tJb4Wyly+e0OHefx2EJtBfUYlZTDvGX5hU+cbYz1mp/KI3PBLSXMwwyqK57WFXNh1N99J1jLqr90AnwXOxHW6/pMIpytAeikzeERbQOvx8S1TKVkSFRKh2OMj71ufrQNo070dwD0t0yhsHIhCcFdwu/GoiStgu9Wrw1vD4aV7or84b4/4Hbh3MCCE4cpULHh4DPraBxYWOGqxOe20RxwF6SAcomWlqX0dXYxtvX3m3n5yxvmX+5Dy0/YFQfNtHXGfS63kvTJNntYZlFX3UVdU4Wtecyrv/tPchwK8VxbfR2vJVhn7jVX2tQ78GXtbwZZJ9AzJq5Sv4SacFhfuSDXZWN1MxNswzinh3NfdMIKvdtZouvgM67ft2wAUEhJfMhKfdO8EP6eI8HKfb0tv1NaPcU7TDOWlVWO84uf+Yu8re5jbZ8by7ppf9DJMpfnYgbyl+0dbIqLsc7HjfXN1LV8af2WD52vGMv83WMsOJlr91SKDnX8L8H6UspJ47r7zZV4Oai2aRjHNNsnAKx0VTSWTbENppR6mjqq/67m0+0fA554o0H014Cn1yT49mw1V1T9ZPsUwGceCVXRGWEdxQF3IWOZDYdRidDJMpcZMb6fxfzNhCoxbAjpCkDHmDepoe5jsvNhY72eASqW0oME5qlkcuw4pFG2kcayma4Gxt92YqimHGC9XhaX6SEWX7ipNrukcora6m5+czVMdJ//t3ff8VGU+QPHPzOzJT0hhYRepClFEBApthNFxe6pp1jPnwVBUO/09DzLWbH3syveWVBOxK6HiCgKgghIR3qvIT3ZMvP8/tjsZjbZZNMb3/fr5cswM7szu/tM+T7l+9idUFp7bG+FtzMwOUf/kYWqN9uqWXlQ/iKTQBEFxFX5Gh2Ls/SfWKx6RlzvxstZxk98bw6o1jGUl8FBzjLm86V5ND/FTGK3asMxnhdq/D5mhAtoZZeCBx2vM84xm5M8j7FPJePBhcfWUhAULcgdpq8mrVx3yocdrzHOd2elr6lrN35ds6qsPK/O+7vx4sbL7c7ABfwd86QK5WCItoYXXU9zv+9yPrFGVHiPF1zPMkRfx/nGD6GKo9dcj5OkFfOk6yVmlFQvMLc/vMdTUmVgHkm0ypPqOtYItDKeb5TVDNtbAS9xfAvAWGMhE2wNdQtLe1OMLHmGHWQA8I37NgCOLHmlxp+nrvSwFkVvxHIdyUOOVznFWMxoz2PkkFjpdpEChWiJosJfo8gim6P03znf+IHzjR/YamVU+fosDnCjYyZTzTFhFSBBZ+o/Mdkxg+t9N7E+wvrgtXyK87WIgXkvbRsj9RX8xzw5tCxZK6xwnrnx8obzMeZZ/XnRPKvC+wR7qEQKymvCH+V2bq8gCz4gnmiUtTbqKEboK9imMshVCXUOyiG891Gg8iY8wLWXu3RyOd1YwHZV9e8a7VplT/AWfNC+wlahVJkT9KUcVAksUz0qrHPUsceLvXLQVc0gvy7BU20r0w3MsGeNNloBo/TlrLY6c4DkCtt313fxruNBvrfK7uNZWjZnGYExtsHrYrQ8EJGux9EqbSINCSufm8aBP6x10+5c48cKyxyYvO58jAXW4fXWIPGYs/o5Ue53TgXgWVfZs4xXVTyvywezMXhKey0EjnmqKzAEK6O0oiKFfHJIpKu2q9KKrYYSY2tN1lAVniejPXm0tZ07kQLeaBUokcpeTfKS3OZ8H4B3XA+FWp2H264xz7meB+AB55uhZfbrXlCMrbwGG3zs2kZoQKhMpMahEUbZMQV7FNhb2SNpS9k+47SKvZciDT+1l72h2prAfm2nmP03cuEvrbiPfC4dqW0I/f1aaYNCtqr8OeJQIYF5A7IXxmedzzPG+IUX/WeyRWWyu4rWy6A57r8AgRqo/5inVGuf0WrKLzFmhy7+kVrT2nKQvzvf4WtzaGiZ/eHpcuNr7nO+xSTvxIiBV9AFxlwecVY+X+INjk+Y7JjBPkdSlcdrl2CrtHjX9RA99R1MKp0CIss2Hiy6ss8z1viZ/zru4yzP/aRpeVxtfMH75okRXxXs1nSb433GGL+Qq+I40vNahe0idW+2i48wXjM+SvdCDcWjjpfZqNpVuV1Vr69KtGO+yviSe5z/4V7f5VW+5hHnq2RoeTzrep4vSo4Ou/ADDCi9ECdoJbjxht20yxuur6z0IcLeg8DQzBr32GvIZFeRWjgqM0hfzw4rA/sHaKvlkKsqBuZn6POJ1Tw1+qhXGF9zpfEVl3r/HqoAiGSnSgv9HVODwDw4rv5PxhxeihB0BkW6MSfYsuW2IY+DhF8L7A/Gtzve43rHZ8y3tQp0jjLM4HnXcwzR1/FH43t6e96qsD74QPWc83nO8D5IJ20vm2twfv3P/Tcg+rlzrjGPkcZKRhorIwbm9cUXpfI2Nso4/8H6Oq5xBFq3tqt0Omr7edM/ptr7NzC5xJjNAuuIUEWI/TeM9ABtLxVTXY/QT99MnooNLeulbeNG10c86b8gtOxyx/94wTyn0uNorx3gSG09ucSHltkryyKVxY7avlAwE+m+WJ/TnkYb1pJIEVcYXzPP6l/rfdQmqLzW+JRbHR9wobdsqMFYYwFD9XXkqVgGeCqOt73J8SFOzeQko2zYjLsWQ7Vi8HKH4x3mWkfyk9UPiFwxkR6lRdx+n0umgB/ck8MqDaIZaywgVSvgJGMJC6zDq/26hhSpRbiPXlZp1lPbziz3bUz3HxfWGyHoAuM7HnO+wrP+c5jkmAnAGqtTtfd/oMBDWoK7xscdZNh+E42Kt+qa9GAxI/RwtHPj5U/GHL61Blb/AKupna03RT99c5XbRjr/7GXTp8qu1Xc5/kNffTMf+I+v9rF4mzB0s+c3GRChp6b9sxtYoZ4jQfYKtUi5pOzDehZszKZf92yGdk2t0zG3NBKYN5Ixpcm7xpcmsqqJwfq6agfmlbUEjtBXcIExl4xygVJ5wZa8c2zdJu1BzH3OwAPus67n+aM5Fy+OsNq74fpKHnO+XGXABWVzKmbUoOvZQL2sdq1n6ZRWyVrFsWl2BiZH62tYYvXAjY8/GnP53DwmtD5YI/iJu2xsrT07dyTBFhn7vu2Jk+Ir6a1QNY2bHP9lhL6Sy7x3VAiMjtbXVBiPVJle2jauMr7iOf+5oWXRgofKAvdh2mr86NzjDCQUudf579C6SDcf+0X3EecrnG/MY4NVFuzYb8IL3BNpU0liQRe+sDHh5Z1lK5+1Gf8Z7fuIREPRjgPsov5uEjGlNefRKk5c+HjeFehyZr+pR/PP0vP1Dud7TPRNqnS7AlswVNsWlQuM79ir2pCvYhmkr+cN89TQukgPVPbrSnBcqv23tCdfut4RyLg73NYqEOn97BWT/bRAt/xoyRYP17fyovNpTjEWM8k7gU+skaXHrIVlxYVAy9PSmOvCuhqWfzgZqS9nja2FMdo46voSreUoWokfbBvrGcy8f5otj0EH9lVZuROp0tf+e9j378BPOrlhQWrwYdfeayzY3TvYXRMCrc/BMZ2RKpDStbwK08jZy1qkc6184q3yajMuvaJAN95oYci9zqmcb8zjr0SfYaHyPdW8G/bfne8Bget20NDSMpFUScVxpDJVm3wap+i/4NAsruPzUNmpzfvYf9uzjR9J0oo5o3QIR3Wk2u5HKkKrZ0OIFphGm+Lv2tJs5Bc4vuch/yVhgTDAQ6UJzIJBOYQH9tEMfuAbNk8ZW+3ty9O1qs+9SAl4a+tmx3+53vEZ/6RiRWxjUmi05WCl04HZn52udnwJwDGu6mc490QZTlldde0oHqlnrr0816SRIsg+XO7H9fv4ev185vz1BLqlx1fxqtZFAvMGVF8PZOUv2521PRSoWLKp2Nr8Z8dXob8vML5DQ/GBeSLvuh6q9f4rC2KOK53SIUeVnTBVBVMQGEd7iv5LldvUlobFtcbnLLV68LMK1Hb/xTGdGxyf8I05CI1AoqNLjG/rtJ9IlQH237qy7MZVUWjcVFpZcZbxE9PNE8LWVzcoB/jUdSduzU9fW61utBbiSK2PCZREHIcVFOlxwlJaaMX5pV1z7d3G3LYxxJUF5UDUip3w79tLWw6yl+i9UDpq+9ivkmoVmE9yzOB8Yx6PRBgfVlvBB5Xyx5NMAUXEhFpL7NPlOSMk74smU8vmB9dkZljHhi0fpq3mdud7zDDLxpFWNzBPtpXJ7toubndMC1tfaMtGHLkSp6wsBL8H+4NbtBZeu3P1H5jifJXrfDfznTWo2q8LOsUITP12reNzPvGOLD1mvUJL6bPOQCu7PbOsvUv+GfoCxjlmk6fiGBChN02w9aomlSv1JVqYEe0a0VPfUdq7A0CRQQ77aMPrzsdYr9qHdcV8xvk8Zxs/hY2ltO9/muuBSpM+Vcci93gMLAZ4XqvWMK/yZXqwtpYdKp3dBHqK2Fsly3fnhopju28wPmaTyuJLa1i1j3mu62b+Zw2pxlCnuk9hZU+aV5UMcrjH+RbvmKNDy5Kj3GuizbBQm4q9SGPnaxMW28twTboGN2e9ShshKl9f1oK5JKZii3lTs5d3HVWhGr0mc7JXNiVdUKQElk1BoYUauYLCe3VWXbpVlMcTRz314NGAp5wvsKWWObEaWjwlFBDH73vyJTAX9cPewltf2nKQ7903A5G73NkFxzZ9Zw6M+r4DtA2VPuBEu9HWJGgMjqOtjWgPNGfqC7ijtNY/+N0Ep4kYbetq1xDzHttrqWuTXMj+yeraOhMMePrZMgHXput2tJtgWTDV+Oz7/KJ0vOwYzxTWqs4VtvUrPfTgN889mdVW51pN/xSsaPibc1qULctEanWNNPWIvWxnageZ5b6NTVYmJ3oD0+q4ajDFXiTBlq/Jenjm8GDFyyB9fWhZdbvt2vMkdLVlyQ2a4iwLTCOduxNs0+dpEbaLNgbd7ilXYCqaqbaEd/YyMlxfyRh9Ee+bJzLTdTeTfZFnTLB/divCmOhILU1DbYHUH0qvM0mV9OQJXpNrU7lSmVsd05jg+ISLvZXnqADCuhtHEqmyyv4wWfbbKB5yvM4ljm+Z7j+Ok4wlnMQSvrXdZ4KJ6m63nSv277YuQTmUteCmkse+alTI2a9/fbXNoTG8wbJiH5/vxF9lsD9QWx8ad2q/Bw/XV2IpPVQpXF4XfS/X6F8wxzyywrpg5ugvraGh3gqN4Z/OqZxuLKxRq3K0ucDrawxzbfKq2MvwxNLZK2q9/yj3v/pS3akkK3NklMSv9XmtqQ17edCxOELbzLoIOT2qI9pzTP8oXcwbS7RnwNo8I9r9X2kre105NTNiroW6qM+z5izjJ141z6jHd2wZJDBvQPV1g7IX9EhjOqKJlqk0gaKwrtzlNeR43JqIdqM+LELNcl0vgNE48ePDEfYdpUSZ+iHSMdlfk0AxSRRUe5xvZeyBdW1aiKMJ/h6jjPqupVYR5+G1c2gVg7Yzjfms9VcMzMu31gbn/m4MKsKoulRbQBupxXy0Hmi97WbrBlbX5H81UZvrVrTKpEsc33KktYH5Vl/2qDYka4VhrWQaqkIgXp/jeoM9eYLd5F5yPR1xO/tnj9TKH+lXiJYYrKGvQRMcnwDwgvOZKFtWLdo1woWPzTGXhC0LzvMN4TlAImmIpFMdtf1cY3zBu1Gy5ts/W6R7qKpk20gitSjHUxwqY71K3qpy5g57wr2yY9rEAH0Tf1XvV7nvSP5sfMkx+iom+SZylvETi6w+UV/TV9vMSH053bTqV1InUEQcnmrkK2m6wLwpKonrep1KraLnWGtgP++nOF/lXONHfjD71eq97OfmDcbHDNNX83vp7C7NSV0TB/4hSiVq81Z/Z2FjJypsLiQwb0C1ma+6MjF46KVtb5AgOSXKjaG5BOY6iuP0ZZVOFRKpe09DPxSfoc/nI+vYsAtItDHqkR787Ms6avv4LabiFCt10ZCB+cmlgWR9uMD4jr86PmCBdUSV250fIYN1+ZvhrY5pbFcZEVs+G0u0G3SkluKLjO8a7HiqY6SxgquMr7nTV/25q6NNWXOeMa/KrOOTHTNC86sH1bWlJ9rY8kjsv1akMduRrifRHh4a+hoUVNcH/Gif45hKpt6p7uurO9VXTbziepIMLTc0FVxl7NPwRUuQGu1+Zx/fqWMxQNvIAduMBLF4ok6pWZnyvWuq4+7S/B/TtAeq3UsvmJW/JlbE/B8At3jLuktHPh/q515Tk3vWkdp6jtJ/b5Jnlf5a/Y2Rbp3Kfsdg6+yxtazMt19jgr1Wequ6zx5R3xKj5D6KVulUftaelqQ+ewo3l9ijsUlg3oBqNsWKqrTVSQPecj3CMH0Ns8zBoeXlkx1Vpq6tbXWdKqa+aKjQXIqR2C/aVxufc4nxLYlRWnHqKpi8K1ILbmUiJbWxB3CXOqo/t3Z1VXaBSyM36njuyt8z2JW97L3rGoQEu/pGm7M5Evu++2qbQi2Jhar2GWXrKtIUUeGtcxV/F/tUKplkM9kxg2Ia7zM8WjqbwvuuyvMLlFfXOX/LB+UQPRBsCPbz0H5tPVufxznGjyRS8YGrsnPrCuPrWnfZbArRepJEG1rQFK0bwfGp0bp/2yvyot0Po13B7FMhnaX/xNOuf4Wtr00yyvpQXw/E0T7/kbb9dIgwB7VexwqYwPRKeo3uJOWT/dWH6j43NWZvppaoPhsFIl1ry8/80hycE+X5RcpM9UiLuah3NantedTxChc65kZcZw9STjbKWieP1DYwxviFF/xnk1/FvOL2aYlqI1pW0MYS7UZtbzEPZvNtaMfqyzld/5lXajAOxhWhJbChW9UquxEsjhlf6/dM1fIYoy8K69rZlDcc+w3aPsa3rkFjXYRlXzU+5zRjEY/7LwwtC954KrtWPOt6vl6SQdVGTVqsW8uDRgxebjBm8rU1NOy3e6Zc8GVnf3hw2SpXgxnx7/Fd0QBH2viinUf1OfSgIUUPzKt/3/6H8+0Ky2qSG6E5ipY0LVqgVdfPb2DhR2/ya0q057eh2hqucHzNp+bwRjqilqk+g6skKg4jiZYLpzlq04JbxBtTQ/T0bAkkMG9ANbkgVRaUV+Uj9z0AJFHA3/3XVLrdhEqSoIzQV5BGHktUjyr30xDJ0moj2o26KWrXTi+dTsieGbU5aojvpi6J/BrCxY453OH/vwoX8+r0KmkMwcqiy42y6UBG6Ct5wPlmWE8Yu6YKymuqtdRsd9H3cpv+AbfxQbVfY+/h0JK7IEZTbMuyH0lcI00L19AWuSdwkvdxtqvKp4YLijSvtkvz1W8GpGYm2n04JULwZHeEtpkv3H9novfGiOsnGB9jaCYrrW61Psb6EK2CYbr7PiBwDReRBPOn1N+9oVstpt9qjuoreVtr1xIrXepD83hibaWq24Jg1XG+zGhZOVMrSf72rushnnM9H5a9uzmL9kDQ0N3Wq5LZzKdmceHnauNzemsNm/isscbTVuYd50NsjLk0LNNtXROx1EWk78Peqhqs2LH3hGmJDtWa7UPJVcZXVa5vLhW40djPyL6l9z77eerWfDzkeC1ituxO2h6uclT9Pbjwk0J+veaYqVzjn3fRzvVLogzFCs6i8bzruYjrb3Z+yCTHTC6u47SmdVXdIQmtPXlbteXvhqcHcIMxkwxy2Bwzjvdd97eaSlvR+GrSe6k1kRbzBlTdsdl1vbVGq5GMFix1r0Fm1qb0nTUwbP7g8qpad6i70vF1g0zfV15m2FydjW9kaeK9+xxTQ8uasit7pIf76sy73NLYp1sT4exz0LdktUmm1xx11cqm9uug7WOl6lqh0vc4YzkL9RvClg3QNlQ5e0nQ4dpWZrmfZ5XVpX4OuApNUSHWLsK4crvYeuo5cUKE7PWNqb7mim71TD/oBnw3BXK2cJtzC2cZ84FAj69NVvOcI1s0f6E8RlrTNvg0Nmkxb0CN1Q3jcH0bDzleq3R9tMC8bRMHU9XlUbXLdNsSRMuMX1eNEZQDxDSTh/fOun2u8Ka7qEfKstyUFQWHggccr3Oe3nwq6YYbzSNHhwiwz+ceDMgj9cZKLpdZuTpBOcAfjcCwtCP0LbU9xKg6avuY557EeOOTBttHZSJN92YXberElqK+KhhatZJcePJw+O9V4C17humjl2VKby3dz0XjCwbmSh1aPfIa5QnxhRdeoGvXrsTExDBs2DAWLlzYGLttMWozRUp5lzgq7/YVLflbcG7f5q41t8q15rGpTS2NyEM5aquuQ0+ay5j35q4oSjb9Ayox4vJLHbN50vVSQxxSrfxcjXmlRdPQqwjMa6sxhlQ97nyJjtp+bnVGzoWQqypPBtvQ7EFZSxavlTT1ITR/K2dC4V5Y+RFNWQEuWqdDdYhcgz8hvv/++9xyyy3cc889/Prrrxx55JGMGTOGvXv3NvSuDzlPOCNnDm4pY/+iacgWCNG6HFQJob/r0nMl0lRrdR2z3pRj3luSoihTxNV2rujGtqKJk1iJygXHv7rqcTx4Y1QgR5tKsD4/z6EqAQnMa0RvmCFaZh0rwlujb82BTX0ItbZPJVd7223VSMDZGjV4YP7kk09yzTXXcNVVV3HEEUfw0ksvERcXxxtvvNHQuz7k2OdqFeJQtr8GF/+qxGsVuzM66jhPb0sJKJtaKlX3ImmnZTfSkdRNfWYlrguvan25DepqWGmAe6rROvIABLWW7uTl5TViT4D6yqngV4dIDymt7HOutjrX29u2xh5mda1s+IOxtH4OpAlkaLnV3jantIFFxpjXI6/Xy+LFixk9enTZDnWd0aNHM3/+/IbctRDiENZR21fl+sZ8wCvPFyVAylOxjXQkzVtrmSqlrkmk6qvFyFWDeelro7m0bJXUIBfJZY5v+KMxl7VWpwY8ovp1fTXGldfl3PGo5psTuCZDDoqVqwGPpPrqWpHbcpSd/3nU3/21uVRs1qddpDX1IbQIh2pG/wYNzPfv349pmmRmhmdlzMzMZPfu3RW293g85OXlhf0nREtTkwdDEVldA2f73NKRJJVL7NSYBuvrqlzvoXk8UIr6UdeHC381s/g3dYVOfeRKqQ/RElDmqPiwfz/ufJmERpxqsy7XtlwVx+3OafV4NBW5tebbDb4mQVpJI11H65pzpEUqyoZl0+DTyWXLrLJyU585G5rLdaU+xckwiWppjZUy1dGs+og8/PDDJCcnh/7r1Knl1GILEdRcMpO3ZE0ZODe0XvqOKte7Wmk31MbWXFr+6pobI9qsGkFJjRhctmQbVPsKy/7pfKvR9l+XbubVraRpSntVSoO9d026NbdpxvOLR8pd0qI8PwQ+uo6wyX7NsmFf7aNMqXeoS23GZbM5cTbjSsKG1KCBeXp6OoZhsGdP+HQJe/bsISsrq8L2d9xxB7m5uaH/tm1rHdk9W7rWPE2ZEM1N+WmaRO3kkBB9o0bQWavbdEGHane+htLUwW1dxi63hKRuMQ04zVhrmWqyOeUA0GpzfSmKEHj7y3qpddT21+GIRGtWkx6lnbS9ZHEA3Ty0pi5s0Kucy+Vi8ODBzJ49O7TMsixmz57N8OHDK2zvdrtJSkoK+080vfpKgnIoO2QSwAjRTDSXqVbqOsbc2cBjww81w/Q1TX0ItdYYU7HVVUP23PA1wx4DtRnP35zGnddbZY9Z9fAxIaBmPUonOWayIOZGMnbMasAjan4aPFq45ZZbePXVV3nrrbdYvXo148ePp7CwkKuuuqqhd92oDslxRqLamtONWIhDQU2yvzYk6WIuRP1IizJTg6i5Ifra+nmjnK318z6tgMQD9ct0NF2y3qbQ4IPwLrroIvbt28fdd9/N7t27GThwIF999VWFhHBCCCGEEEJE0lpmamhOatyzSFWy/f56CvBbASmn9csyYpr6EBpVo2THmThxIhMnTmyMXTWNwgNyIgohasWvdOlRIYQQotFlkFOzF0iXddHIDrUWcxn4Wh9ytrBLpTbIWzeXuWGFEA1jNw1z7RBCCCGqcoqxuGYv+PDqhjkQISphGk07FWhjk8C8PnQ4itGex3jHf1K9v3VrnMOxoX1mHsPPVp+mPowmsdjqyXP+c5r6MKott47zlbcGcZSwxWrb1IdxyNhTyXROi62eXOe9iXt9lzfuAYkm8Y05iN2qTdiyW7zX84b/1Fq/52KrZ10PS4hGlVbTXByrP22YAxGiEqZDAnNRQ0opConlTn/VNYnT/cdVuf5rc0iN9z3JO6Ha23pVWUbTvBYWEC2xelR726/NIVzkvTts2QLrcM7z3Mt47+SIr/Gp5pfttTbmmkfyhP9Chpb8K2z5rb5rK33Nd+aRdd7vld7bavW6W3zj6Vrybq33u8jqVevXNhepWgHHe5/miJI3Iq5vTb1m7vNdxrXem2v12hnmqDrt+zPzGP7geZyX/WdGXD9Y/52vraOZao4JW77c6lrpe/5s9eFW37UUK1eNjqUlVZ7ZVXYPu9V3LS/5z2Sq/5Rqv9dFnrvq67Bq5UtzGH/3hd+z/Ti4z395rcvon7xN+5mEqKn4BpzerrXzqEYZDcwC6/Aav+Yy7+0NcCRNQ2mN8z03FxKY14NIuTCWWocx0xzBxd47Q8v+7v8/PjWPYa9K4XuzP9+b/bnRWzb2/jrfLbzuP41n/OeGvVehcgOwQ6WFLfcqgwXWEVzu/RsAv1sdAPiX/6zQNrPMozjO8xRv+E/lNO8UPMpJkXJzvOdJHvJdXKvPe4bngdDf35v9K6zfp+o2zd0l3r/ziTmctVbH0LJzvffxH/9oAFZEeFA+3vMk35oD2aeSmWMNBMqC0Vf8Y/mT9y5+Vb340hrGqZ4pfGsODHv9znLfLZQFfcHvvyr3+i5neMlzEdf9Uo3gcbHVk/M894YtO99zDxutLIaW/IvuJW9zeMkbfGUOBeBK763c67scj3JyqfcO3vefwC6Vyttm4DvaR0rofS7z3s508wT+5rsm4r6f8p9f5bHd7buiwrENKHmF3iVT+d7szxfm0aGyB3C996bQ3/tUEk/7z6v0vX+y+la578/No0N/zzRH8Kl5DD1K/s3IkmcYXPIiT/gvZI9K4TX/aWGvK18GJ3gncYv3ekaWPBNxP4/6Lqqw7EJb4PC47wJeL7ePaB6u4flVRFmCk4/NEaG/r/bdGnH78gH7OO8dYYFG8Gb+uO8CPvAfz1LrsNC6783+ESvnviwtX3Y3e8czz+zLdpUetUWwqvUe5eQN8zRW2s7ft/wnh/5+339Chdc84BsX+tvekh1pW4DRnkcr3X+RcrNRtedTs+JUneE0LvbeyT6VzJ+9f+Vc7338wfM4U3x/CnsvgEneiUw3T+Bwz5tMKr2Wv11Jz6l5ZqCszzBH8YT/Qh73XVBhmxnmKE6u4jNA+PVog9Uu7B4CMMYzhe0qPawyboJ3Uujvp3yB8/1+36WhZbPMwaG/3/WfCATOt6BFVi8u9t7Jrf7rOc9zL3kqjrt9V4TWb1cZTPFfzOP+C0PL7A+G7/hPYpuVwdP+8+ha8i5dS95lkerNGqtTpZ9zmdW9wjJ7GS5/P7SrToVdjObFVy7NztdWoHL8f1b0SvLNVsUEtuXfD+Bq719Cf4/z3sEyqzt/9Nwd+h3+Zw7m8JI3uMZ7S8T92Hvi1WdPsDGeKXxoHssoz9Nc6r2DtVZHclR8tV9f/h5aW/bPdLH3TgaVvBS2/h++q/jEHB72LGUXPO/s99m9lfSKWW51jVgJP91/HMd5nor4muD3f1AlVP4hgA/NYyssy1dlLX3B19uP01ODOZ0h/LpX2fcRyTyzLwut3uxSqaHnxaAj9C3QRFNLvukfU2HZDpUW8boQvC7ZBZ8J7ezPpG/7T2KF1ZWPzJGhZed6/gkEzt/jPE/xnXkkn5nDIh5fpGe/jVaW7VjTI76uMvYeOtusjNCx/mhW/Rw0z+wX+vtV/+lh6/aqFI71PMUPZj8OqMTQ8k2qXejvf/iu4hTPI+yP8GxuKS1iw81/zbJK2EjXOoD1VnsA/m27j0eyyupSYZn9Ob6qc2unSqUk9tBKFq4pVVmKxaaXl5dHcnIyubm5zXpOc8tSdP/7FwBsjrkEgMu9f+N7K9gSqQAt9LeGQoXqRBTH6KvZYLVjH2Un7avOxznZ+JUi5eYC7z38xfEBj/kvQkNxgr6Ut82TceMLBWAO/Pgx6KLtYZtqSwoFnGv8wEfmsWRT9t1lko0XBwdLlznw85rzCf5tnszdjv/QVd8DBG524xzfMMk7kZ9jAje+q7y38pPVFw8uZrr+wUB9Iyd7HqUYN3kqjrnum2mjFTCs5Hn2kMpFxhwecb4KBIKUO5zvAYGLxNvmyYBic0zgwTtHxXOJ9042qPZ4CLQ+JVHATY4ZfGSOYrnqDijaksNYYwH3OP8DwNElL5Ch5bJSdUXDwomJl+rd7NLI5QXXs0zzn8gWlcnrrsd4wn8hDzrfwFQaAz2vcp3jUz4yR9FR289brkf42BzB4doWeuk7+KvvOq4yvuJzcxj/Ms8B4CR9Mb21beQTx3TzeIbrq/jF6s3ymP8L7fcx34WMMRZxq+86HnG+ylT/GGZagdbAbtou5rj/wi3e65lhVWyd0rBw4Q99R078todBezmDZArI0rJZqzqHlo3RF3GLYzov+89gjeqMFwfrVUe6ars4qBK52/kfMsjhUf9FTHB8TI5K4A5/IKCf67qJLvpetqt0RnmeLXdkZb/lWM+D7FJpjDO+YZp5IvtoQzsOMNv9V+I0D7PMwTjws9jqxfNmoBIqhXyWxlzHnb4/s9Lqym2OaTzov5RtKp0bHJ8y0xzJGtvniCR47k303shsaxDJFHKT40P2kMpT/j9W2O5p/3k8HVqumGzMYJvKYKnqwRHaFj6zhjNCX8Fo/Vce8f8JDy6mu+5lqL4OCLTAblfpdNL2cr/vMiY6ZqKjOM/4gfO997JSdeNSYxaD9XUcp/9GmhaY6udbcyAD9fWstLpyrLECINRr4DLjf5yuL+Ra3y2cb3xPV2039/qvCH23s8yjGKav5j3zDzztP5+LjTmca/zARtWeyb6JaFicp8/jF9WLLSqLDA6WXiMC5ULH4hh9Fb9Z3TnVWMTjzpd5wX8WL/nPIgYvfnSWxFwf+q4e8f2JF82zQt9RJge51/kWb5ljaEsO/3C+zV2+q3jM+TKzrUE87ruQ6x2fcoExNzRf6T99l6Fj8a55EsWllQ/vu+6jl7adUz1T2EsKh2k72aDaM86YzXB9JdtVBgdUEq+YZ5JBDl4c5JLAs87n6Kzt4UbfjfzgvhmPcjLRdyOvup7kQ3MUf/HdQEdtH/Pck9mvkjjO8zSrYv4MBCrqppsnANBP28jTzn+x0OrDJY5vQ+V2pepWRQlTHKsvZ5XVhQMkV1kWNSwcWJyl/8QTrpc4y3M/y1U3+mmbWas64cWJjsVU5yMcJJHJvrLgOp1cfokZD8Dr/tO42vEl+1USL/rP5Bh9NX/xjWe4vorfVQc2qsCD0cXGbC43ZnG5929h95HgcYOGhlV631H01HawSWVxmTGLc415XOH9G3c43qOzvpfLvbfTVsthp0ojk4O00w7wqwoPdIPv9aTzX3TQ9nOp9++h61AsJbTTstmo2vOA43WytGyu8f3Fds8rf2xwk+NDbnLM4ENzFLPMIfTXN/KG/zS+cN/BfOsInPjpqO1nkm8iW1Qm8ZQwWl/MM67wnkEP+MbhxOQN81R8ONCxiMPDUve16Jrib75reMT5KjkqnlM8j5JDAutiAhUMd/r+zDtm2YN+F203Fxvf8oL/nND1+1n/OeSrOA7Xt/Kj2Y8nXGVB5PP+s3ncfxEXGN/xmPOV0PKuJe9ytLaaeK2EOdag0HIdi4Haepar7qHvLnht+qPnbjK1HNaqjuxRqdzl+A8fWaP4zerOypirmWf25SXzLN52PQzAWM9D5BLHbpXK7Y73+D/Hl4z3TiaXeK43PuUZ/3lY6OQSz7fuv/Kz1adCr7LgMfXTNrFCdcNC5zh9GRus9nzsvot0LY9Z5mB+s7pxveNTLvPeQV99M121PSyyeqOAU41F/EFfwjne+3nR+TR99G0ATPbewGD9dy53BOYjPqLkDfpoW8nUDvK9NYBZ7lvZaLXjUt+dYd9D8PsLsi8HGOV5hu0qg2AZb0MecXjYQXromrnC6ko/fTNjPFNYqzqhoeirbeZ84wd+tPqx0OpNHoHA4ErjK+Io4TbnBwCstjpxofceRugr+cnqy+POl/jB6s88qx/Fys0Ex8dc7pjFWZ77Was68ZTzX8y1juRK42sO17fyoXksd/muwo2XXBIYrq9kmXUYx+rLudP5DhO9kxhnfMMwfTX3+y/jZedTPOS/hEVWb0bqK3ndPI0rjK/5wRrARtUODy7SyKWrtpvFqjfD9ZXc4XiXW3zj+cYd6LW22OrJNPNEdqo00snlTGM+t/uuJZtEDCx8OBiqrcFEZ4b7XiAQrC5RlVeqbp4ytuwf91Z97bPLV7EkRpgucqnVncf9FzHfOoINMZcBgd5U21U6v1i9idM83GDMZK3qzD+db+FXOsM8LzBYX4cXJ258nGnM52++axilL0eh8T9raOg5+DPXnfTTNzOi5Fl2kk4XbTdz3beUlqd3CNwPy56XnPj5PSZQ8fuI70/8avWkt76VWeYQ3nY9xE9WX/rqm1lndeR+/2Wca8xjl0rldGMh5xs/AIH7y8OO17jFN55nXS8AsMnKJFkrpFDFYmgm13lvIYd4bjRmcr//MtpqB7nMmMVL/jO50/kOHbX9vGv+gTP1+Tzpv4B8YjlFX8yb5hj+bHxFhpbDW+YpfOcOVPZd6r2DeVZZRcRh2g5muwMV+d1K3ma0/isn6b9yj/9KPLg4SV/M664nQtuP8jyNUho7yKCLtpsdKh0/DrI4wG5SOVefhw8HX1lDudj4lp+svqH3D3yXgXPTjZdLjW/op2/iXONHIPCcU0gM+SqOR/0XcbbxEwutPgzW1/GheSzFuEkjj2wS6att5nbHezziv5iVqitHaJv5yHUPz/vP4TnzXF6+/GhOPqJ5B+f1Ga9KYF4PTEtxWGlgnsUBeunb+d4agD1IqqlU8pjkmME08w9RA5L6EksJkxwf8YU5rDQQDujAPpyan822GjgnftqQz94KD4FldCxedj7FZpXJg/5LbQ+GZTLIYZJjBu+Yo6v9ObM4wIKYG1lhdeUM70M1/JRVCVyokynAQief8BbF4PEnUUAvbTu/qN5U9zc+QtvMF+6/84z/vLAgsSU5VV/IBMdMbvddy0rVtcL6Y/RVdNT28V/z+ErfI55iinBX8pBeN721rRym7eQL65gqt8viAN303cyP0lofSQf28ZDzddapjjztP59Cqjf26THHS1zg+J79KokhnhdxYJJIEbc53me6eXyFwKe84APpm/4xPOC/FJP6GHqh6KztZZvKCPs9TtSX4MHJT1a/Kl5b9h6g4cKHFwfB8yGTbE42FvONeRS7qdiy6caLEz8F1H5ITVdtF3kqnmySyOIAB0gOBTjJFFBEDD4cHKbt4Gh9DdPMEyOWu/bsZz/J1a7QawzXGZ/iw8E080QuMWbzlTWU7ap15yGIpYQSXGG/UaR7RpATPzcYHzPHGogPB7tUKjkkRtzWwKSdls12lYELH258oev7WH0Bx+vLuNN/dcQWb4CR+nJG6St4wn8B/tJtdCwec77Eb9ZhfGAeH6p0Auio7eMWx3Re85/OqgjXysocoW2mu7aLz6xovToCumq7aK8dqOa5GtCe/WSTSAnRe4IFHabt4CLjO172n1FaKRVeCVxGEYOXEtwcpu3gCedLPOM/L1Qh0Z796JoVoSwrdBRW6W/dW9vKI85XedR/UdhnO0Ofz1hjAbf6rot67ThVX0g/fVNpL46aPYvFUYIDEx9G2O8aiYFZ4XqcQQ5jjEXMNEdW8xoX+D7deEOV7jWVTi4XG7N53zyxyucyu+B95QHfOF4zx1a+XS0D841WFt313RWW2ytbhmprOMFYytP+P0Y8/zQsYvGG9SiLxomfRIrCGqUuNWZxUCXyeSXPB4O1tZxkLOFp//nVvhekkM9Ex0z+ax7PGtU59PudrP/CjY6PuMU3nvWqA3WJBcrTsdgYE+jt1LtkaoXy8n/G5+xVKXxijYz0cpIo5B7nv/nQPLZG142gY/RVvOF8jPv8lzHN/EPYOgOTo7TfSdCKWWj1qfbzUTQvjjuK0/q3i75hE5LAvJnZvL+QEx7/rqkP45DShjwKiW1WD9NCVCaeYv7P+IJPreGhVs6aGG98wsXGbC7w3sMeyeIuhBCijq4zPuUO53t4lJPenrcq3a7KwNydBAMuhEWvhS3er5LYpVLpr2+u8H51ySsjAhU/TvzspGZd6etLpMqohjR2QDteuOSoRttfbdRnvCpjzOtBnLt1JA5rSQ6SJEG5aDEKieUZ8/xaBeUAL5pncZz3GQnKhRBC1IvNKjBe2q35GKMviv4C0xf+78z+cMc2yKzY+2yN1YkZEcbdi7rbR0qTBeVAowblAB1SJCu7qKG0+Op3CWtJxg6oXdeR+8/uy7BuzS+AaIkn91GdU5r6EKJKjXeRkdg6z4HGkuB28Nu94Rmtj+7auOfQB9dVr/vsoe4fY2ueIVcIIZqb1bbhg887y+eNicBXbrz41V8H/p+QVWHTUcZKlltlOTue9DXNEL6W8AwlqjZuWOMM520uDq0c9A3E0DVW33cqplK4DB2HrrFg0wHm/b6fm0/uxY6DxbidgToQpSAhxoHL0Pl5UzZHdkwmOdbJyp15JLgdtE+JZWdOMZ1S49AAUymchs6evBIKPX6SYp2kxgXGlGzYV0Csy6BjmzhKfCbFXhOA5Fgnu/NK8JkWmw8UMaxbKh/+up3u6Qnkl/g4pW8WG/YV4PFZFHj89OuQRKHHxKFruBw6sU4Dr2kR4zR4+LxADWlukY/9BR68fouju6ViqcDnXr+3gK5pceQW+1iwMZvT+mWh6xqXDe/Ktuwicop8JMY46NAm8LnSEtzEuwx8pkLX4LV5m2gT5+TCIZ0o8Vm4HDob9hWQEuvkt+257MorYX++h7MHtg99D72zAuMIV+7MY0DHZLYcKKJLWhyb9hfyybKd9M5MpFNqHG3iXKzbk0+frETaJsWQ4Hbg9VvsK/CQkeBm28EiEtwOEmMcGLpGkcfE5dCJcxkcLPLhMy1iXQY/rT9A3/ZJZCbFsG5PPqal6NchOfTbr9yZi2VBz8wEft9TQEaim+0HixjcpQ2aplHiM1mw8QBHd0vFoess3JRNVrKbzqnx5JX4SE9wU+w1OVjkpU2ci20Hi0hPcOM0NBLcDvKK/bgcOroOe3I9rNuTT++sRJSCDm1i+WVzNiV+i+7p8STFOPl120G27C/k3EEd8VkW8S4HfstiW3Yx8W6DWJeB31TEuxxsyS4kNd5FvMvB0u05mKbi+N4Zgc/uNNiwrwBD18lKiuHnTQfokBJLp9Q4Vu7Mo3dWIgnusktIdqEXh6GRFONkR04xmaXBus9UfLx0Bz9tOMDNJ/dCKUWR16RzWhy6plFQ4kfXYOWuPFJinQzq3IY9eSUcLPKyeX8RJ/bJYNGmg+zMKea0/lnEuRzMXr2HtAQ3mUluspJi+Hz5Lkb2SGf93gKO7JjC8h25dGwTS7vkGDx+C9NSrNiRS0qci6ykGDQdlm3LoW/7ZFLjXRR5/bgMne/W7qN/x2TcDp1l23NpE+dkQMcUTEvx6Ndr6JIaz1FdUuiQEovbYYTO1SKvn3iXg1W78pi7bh/nHdWBb9fs5fB2SRyWkcCm/YXkFfs4UOihY5s4hnRpw9crd+O3FEO6pJKVHBg/t+nh01myLYfemYnEuQzmbzjAbztyuWhIJ5wOnewCL06HRrvkWFbsyOXLFbvo2TaR43tl8OWK3bRNdJOR6CY51klyrJOl23LokhZHt/R4PH6LD37ZxsBOKSTHOumSFs/KnbkUeU2GlJbVhXeexDer9mIpxXlHdUBDY9bqPRzdNRVLKUp8Jp8u20XvrASGdk1l6bYcerRNwGdaZCUHKr6++G0X2w4W0TsrkZP6ZLJyZy45RT6GH5aG26Hz6W87SXQ76ZmZQOfUONbtKWBnTjGaBv07JDPj1x0M7ZZK59Q4Yp0GL8xZT7HP5NxBHfBbiv35Hn7bkcvY/u3okhbHjpxiYpwGa3bl0T0jAUPT2JVbTGKMk+4Z8ezL9/DpbztJT3Bz/lEd0YDNBwqZvng7Jx+RGboWtE10h66lybFO1u0pYE9eCX3bJ/HqD5tIT3Bx/lEdaRPv4upR3dA0DZ9pYWgaS7YdJLvQx2EZ8WiaRttEN8t35LI338NxPdPZtL+Q3GIfy7blclyvdPYXeHnif2t59fIhdEqNY8HGwP3isLbxnDOwAztyivlg0Tb6dwz8Vg4jcH1KT3QR4zDYV+BhaNdU/rdyN2t25zN2QDvmrNlL+5RYjuuVwZKtB2mXHMOizQfpnZVISqyTf8/fQo+2CfTKTAxd8/YXeOjUJi5UhnfnltChTSwrduTRLT2eb1btweM3OSwjgR6ZCbSJc1FQ4ic5NtBb6ccN+0mOdRLrNOiZmcia3Xl0To1jW3Yx7VNi+HbNXrYcKGJM3yx6ZSbw3dp9fPjrdi47pgvtkmN5ce56BnVuw/G9Mmib6Oa9hdtwOXSO7JjMgUIv7ZJjiHUa5JX4aJ8Si6XAb1okxzrZcqCITfsL6Zoej6UU3dPj+WnDAX7fk09Wcixj+mZS7DPJK/bz04b9dEmLo1+HZB74bDWWUtx2ah/25ZfQKTWOnzYcwGXoDOyUwooduSzYmM1xvdLpnpGAx29iWVDsM+mWHs/qXXnsy/dQ6PHTr0MyaQkufKZif4GHBLeD9AQ3/56/mSKvyen927FmVx5xbgdd0+LYk+fh8HaJrN9bQKHHpF1KDAcLvXz22y76tk9i9OGZJJWetz7TYmCnFFyGzvu/bKNdcgzpCW56ZyVS5DGZtXoPJ/bOIDXeRV6Jn437CnhhznpuObk3y3fksGjzQf4x9nD25XtIinXy0twNxLkMkmKcHNszg4+X7WDiiT3IKb3XLd5ykMykGNqnxJIc62Tt7nxGHJaGAt5esIV2yYF1beJdpMa5mLtuLyt35pGZFMOQrm1YsjWHpVtzGNUznT/0acv+Ag978z18snQnJ/bJQKlAJW6n1DiSYpxszS7C67fYk1/CEe2SSHA7Sr8XP8O6p1FQ4ifebWDoGhv3F5KZFMOevBJinQbpCW7+s2ALS7Ye5MoRXemcGofPUny6bCeJMQ7OOrI9flNR6PVT7DXJ9/jxm4oBHZPRNMgt9oGC6Yu307FNLOv25DO0aypHdkxh9pq99M5MpFtGPD9vPMCm/YVYSnHGgPb4TIs1u/PZlVPMFSO6smJHHu8t2sqwbqmln9nLtuwikmKd9G2fxKb9hbRPicVbeg9asvUgLodOeoKbtAQXhR4/6/YU0D4lcK9KjXexLbuIZdtzGNipDd3S49lyoJBCj4nbqdM+ORaXQ2flzlz8lqJf+2R+3XoQQ9cY0DGZbdnFvL1gC23iXGzJLuSvp/TGoWs4DJ3kWCcFHj9Lth4E4N/zt3DNscfAfwJTAzo1k+X3nsK6PfkUeU3i3Q7O+9dPuAxb+509MJ+wCFylWfwTKibmmlNuGtZnzXPZTRtW2zJ0x7kMTu/fjttP60Ox1+SluRu4aGgn0hPcKOD+T1dh6BqTR/ck1mmwfEcup/bN4scN+0vv2yYFHj/tk2NplxLDut35ofJZ5DNDjTEHCjw4DJ0vl+/i8+W7OKZ7GmP7t6NtkptXvt9ITpGPccM60zMzkR9+38f+Ag+dU+NIiXPROTWOh79Yw6DOKRzTPY0PftlGsddkxGFp9GibQNukGLYeKOLNnzZx4x96MnfdXgo8Jl3T4miXHMOu3BIGdW7Di9+t5+pR3Zm9eg87c0pITXDRJs7J+r0FHCz0clSXNpzevx0ev8UP6/aR7/EztGsqmw8UsmV/IScdnsmBQi9tE90s3nKQXpmJdEmLY9m2HPYVeMgt9tE7M5GBnVL4fPkukmMD5/na3floGqzfW4DfshjaNZW0eDe/bc+he0YCqfEufKbFlyt28f26/Yzpm8XIHmks2pxN3/bJJLgd7MotZtP+ImYu3cGoHulcfHRnSnyB7z670EtKnJNPl+2iXXIMJT6TU/tl8fueAlbszCW32Mfh7ZLYtK+Q0/u3I85tkFvkI7/EH4hX4pwcLPTy1Kx1/OnoznRNiyMtwY3fCpwz8W4HSTGHVu9YGWMuhBBCCCHEocY+Zvze3NCfu3KLOfbh/6EZTn5/sHSKruxN8OxAcMbBnbvKXpe/B54IT2B6s3c886x+LIqZAEQeVx42dl2IFkzGmAshhBBCCCHqh7cw9GfSd3exyH0DPdhWtt5fEvi/s9ywwIS2kHE4JJblUPHgZB9tONtzHyd5HmvIoxaiVZHAXAghhBBCiEPN2S+U/b1jcejP+CWv0kYrYJLx37L1vqLA/x3lAnNNg+vnwaRfQ4uCyXmXqR5sUB3q/bCFaK1kjLkQQgghhBCHmkGXwm8fwKa5MP1KuGEBxGeEVp+mL4S3zw/8ozgn8P/yLeYAhgMMBwdUImlaPoutng1+6EK0RhKYCyGEEEIIcSjK6h8IzIsOwOM9YfJv4evXfxP+7+SOlb7VSM+zxOAlh8QGOFAhWj8JzIUQQgghhDgU9f8jzH++7N/PDAhff85LZX9rOhx2YqVvVYKbEmT6VCFqSwJzIYQQQgghDkXtBla9fuDFjXIYQghJ/iaEEEIIIcShSdPgL2srXd2MZ1UWotWRwFwIIYQQQohDVVx6xMUXeu5C4nIhGo90ZRdCCCGEEOJQZTjgr78TiMIV1osj+XPOn1moDkficiEaj7SYCyGEEEIIcShLaAuJmZCYRe6E1XxnDQSkK7sQjUkCcyGEEEIIIQQQGHYeJGG5EI1HAnMhhBBCCCEEABplkbk0mAvReCQwF0IIIYQQQgRo0TcRQtQ/CcyFEEIIIYQQQPmu7NJkLkRjkcBcCCGEEEIIAYQ3mEtXdiEaT4MF5g8++CAjRowgLi6OlJSUhtqNEEIIIYQQop5omvRlF6IpNFhg7vV6ueCCCxg/fnxD7UIIIYQQQgjRQKTFXIjG42ioN/7nP/8JwNSpUxtqF0IIIYQQQoh6FNaVXcaYC9FoGiwwF0IIIYQQQrQs9p7sZzw3j9Q4V9MdjBCHkGYVmHs8HjweT+jfeXl5TXg0QgghhBBCHFocetlI1437CtlIYRMejRCHjhoF5rfffjuPPPJIldusXr2aPn361OpgHn744VAXeCGEEEIIIUTjcjl0/nJyLxZsOsBlx3ShuhObr96VxzOzf4+4rnt6PBv3BwL8N68aWl+HKkSroilV/bQO+/bt48CBA1Vu0717d1yusi4vU6dO5aabbiInJyfq+0dqMe/UqRO5ubkkJSVV9zCFEEIIIYQQQogGlZeXR3Jycr3EqzVqMc/IyCAjI6NOO6yK2+3G7XaH/h2sM5Au7UIIIYQQQgghmpNgnFqDtu5KNdgY861bt5Kdnc3WrVsxTZOlS5cC0KNHDxISEqr1Hvn5+QB06tSpoQ5TCCGEEEIIIYSotfz8fJKTk+v0HjXqyl4TV155JW+99VaF5XPmzOGEE06o1ntYlsXOnTtJTExE06o3vqWpBLvdb9u2Tbrdi2ZLyqloCaScipZAyqloCaScipagJZdTpRT5+fm0b98e3ZY4sTYaLDA/1NTn+AIhGoqUU9ESSDkVLYGUU9ESSDkVLYGU04C6hfVCCCGEEEIIIYSoEwnMhRBCCCGEEEKIJiSBeT1xu93cc889YVnlhWhupJyKlkDKqWgJpJyKlkDKqWgJpJwGyBhzIYQQQgghhBCiCUmLuRBCCCGEEEII0YQkMBdCCCGEEEIIIZqQBOZCCCGEEEIIIUQTksBcCCGEEEIIIYRoQhKY14MXXniBrl27EhMTw7Bhw1i4cGFTH5JopR5++GGGDh1KYmIibdu25ZxzzmHt2rVh25SUlDBhwgTS0tJISEjg/PPPZ8+ePWHbbN26lbFjxxIXF0fbtm259dZb8fv9Ydt89913HHXUUbjdbnr06MHUqVMb+uOJVmrKlClomsZNN90UWiblVDQXO3bs4NJLLyUtLY3Y2Fj69+/PL7/8ElqvlOLuu++mXbt2xMbGMnr0aH7//few98jOzmbcuHEkJSWRkpLC1VdfTUFBQdg2v/32G8ceeywxMTF06tSJRx99tFE+n2j5TNPkrrvuolu3bsTGxnLYYYdx//33Y8/fLOVUNLbvv/+eM888k/bt26NpGjNnzgxb35hlcvr06fTp04eYmBj69+/PF198Ue+ft1EoUSfTpk1TLpdLvfHGG2rlypXqmmuuUSkpKWrPnj1NfWiiFRozZox688031YoVK9TSpUvV6aefrjp37qwKCgpC21x//fWqU6dOavbs2eqXX35RxxxzjBoxYkRovd/vV/369VOjR49WS5YsUV988YVKT09Xd9xxR2ibjRs3qri4OHXLLbeoVatWqeeee04ZhqG++uqrRv28ouVbuHCh6tq1qxowYICaPHlyaLmUU9EcZGdnqy5duqgrr7xS/fzzz2rjxo3q66+/VuvXrw9tM2XKFJWcnKxmzpypli1bps466yzVrVs3VVxcHNrm1FNPVUceeaRasGCB+uGHH1SPHj3UxRdfHFqfm5urMjMz1bhx49SKFSvUe++9p2JjY9XLL7/cqJ9XtEwPPvigSktLU5999pnatGmTmj59ukpISFDPPPNMaBspp6KxffHFF+rOO+9UM2bMUID66KOPwtY3Vpn88ccflWEY6tFHH1WrVq1S//jHP5TT6VTLly9v8O+gvklgXkdHH320mjBhQujfpmmq9u3bq4cffrgJj0ocKvbu3asANXfuXKWUUjk5OcrpdKrp06eHtlm9erUC1Pz585VSgQuprutq9+7doW1efPFFlZSUpDwej1JKqdtuu0317ds3bF8XXXSRGjNmTEN/JNGK5Ofnq549e6pZs2ap448/PhSYSzkVzcXf/vY3NWrUqErXW5alsrKy1GOPPRZalpOTo9xut3rvvfeUUkqtWrVKAWrRokWhbb788kulaZrasWOHUkqpf/3rX6pNmzahshvcd+/evev7I4lWaOzYserPf/5z2LLzzjtPjRs3Tikl5VQ0vfKBeWOWyQsvvFCNHTs27HiGDRumrrvuunr9jI1BurLXgdfrZfHixYwePTq0TNd1Ro8ezfz585vwyMShIjc3F4DU1FQAFi9ejM/nCyuTffr0oXPnzqEyOX/+fPr3709mZmZomzFjxpCXl8fKlStD29jfI7iNlGtRExMmTGDs2LEVypKUU9FcfPLJJwwZMoQLLriAtm3bMmjQIF599dXQ+k2bNrF79+6wcpacnMywYcPCympKSgpDhgwJbTN69Gh0Xefnn38ObXPcccfhcrlC24wZM4a1a9dy8ODBhv6YooUbMWIEs2fPZt26dQAsW7aMefPmcdpppwFSTkXz05hlsjU9C0hgXgf79+/HNM2wB0eAzMxMdu/e3URHJQ4VlmVx0003MXLkSPr16wfA7t27cblcpKSkhG1rL5O7d++OWGaD66raJi8vj+Li4ob4OKKVmTZtGr/++isPP/xwhXVSTkVzsXHjRl588UV69uzJ119/zfjx45k0aRJvvfUWUFbWqrrP7969m7Zt24atdzgcpKam1qg8C1GZ22+/nT/96U/06dMHp9PJoEGDuOmmmxg3bhwg5VQ0P41ZJivbpiWWWUdTH4AQonYmTJjAihUrmDdvXlMfihBhtm3bxuTJk5k1axYxMTFNfThCVMqyLIYMGcJDDz0EwKBBg1ixYgUvvfQSV1xxRRMfnRABH3zwAe+88w7vvvsuffv2ZenSpdx00020b99eyqkQrYi0mNdBeno6hmFUyCS8Z88esrKymuioxKFg4sSJfPbZZ8yZM4eOHTuGlmdlZeH1esnJyQnb3l4ms7KyIpbZ4LqqtklKSiI2Nra+P45oZRYvXszevXs56qijcDgcOBwO5s6dy7PPPovD4SAzM1PKqWgW2rVrxxFHHBG27PDDD2fr1q1AWVmr6j6flZXF3r17w9b7/X6ys7NrVJ6FqMytt94aajXv378/l112GTfffHOoR5KUU9HcNGaZrGybllhmJTCvA5fLxeDBg5k9e3ZomWVZzJ49m+HDhzfhkYnWSinFxIkT+eijj/j222/p1q1b2PrBgwfjdDrDyuTatWvZunVrqEwOHz6c5cuXh10MZ82aRVJSUugBdfjw4WHvEdxGyrWojpNOOonly5ezdOnS0H9Dhgxh3Lhxob+lnIrmYOTIkRWmnFy3bh1dunQBoFu3bmRlZYWVs7y8PH7++eewspqTk8PixYtD23z77bdYlsWwYcNC23z//ff4fL7QNrNmzaJ37960adOmwT6faB2KiorQ9fBHdsMwsCwLkHIqmp/GLJOt6lmgqbPPtXTTpk1TbrdbTZ06Va1atUpde+21KiUlJSyTsBD1Zfz48So5OVl99913ateuXaH/ioqKQttcf/31qnPnzurbb79Vv/zyixo+fLgaPnx4aH1wGqpTTjlFLV26VH311VcqIyMj4jRUt956q1q9erV64YUXZBoqUSf2rOxKSTkVzcPChQuVw+FQDz74oPr999/VO++8o+Li4tTbb78d2mbKlCkqJSVFffzxx+q3335TZ599dsQpfwYNGqR+/vlnNW/ePNWzZ8+wKX9ycnJUZmamuuyyy9SKFSvUtGnTVFxcnExDJarliiuuUB06dAhNlzZjxgyVnp6ubrvtttA2Uk5FY8vPz1dLlixRS5YsUYB68skn1ZIlS9SWLVuUUo1XJn/88UflcDjU448/rlavXq3uuecemS7tUPbcc8+pzp07K5fLpY4++mi1YMGCpj4k0UoBEf978803Q9sUFxerG264QbVp00bFxcWpc889V+3atSvsfTZv3qxOO+00FRsbq9LT09Vf/vIX5fP5wraZM2eOGjhwoHK5XKp79+5h+xCipsoH5lJORXPx6aefqn79+im326369OmjXnnllbD1lmWpu+66S2VmZiq3261OOukktXbt2rBtDhw4oC6++GKVkJCgkpKS1FVXXaXy8/PDtlm2bJkaNWqUcrvdqkOHDmrKlCkN/tlE65CXl6cmT56sOnfurGJiYlT37t3VnXfeGTaFlJRT0djmzJkT8Zn0iiuuUEo1bpn84IMPVK9evZTL5VJ9+/ZVn3/+eYN97oakKaVU07TVCyGEEEIIIYQQQsaYCyGEEEIIIYQQTUgCcyGEEEIIIYQQoglJYC6EEEIIIYQQQjQhCcyFEEIIIYQQQogmJIG5EEIIIYQQQgjRhCQwF0IIIYQQQgghmpAE5kIIIYQQQgghRBOSwFwIIYQQQgghhGhCEpgLIYQQQgghhBBNSAJzIYQQQgghhBCiCUlgLoQQQgghhBBCNCEJzIUQQgghhBBCiCb0/yamXkLHqn+hAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axs = plt.subplots(2, 1, figsize=(10, 3))\n",
    "\n",
    "ax1, ax2 = axs\n",
    "ax1.plot(fy, label='original')\n",
    "ax1.plot(context['fy_hat'], label='predicted')\n",
    "ax1.set_title('fy')\n",
    "\n",
    "ax2.plot(ry, label='original')\n",
    "ax2.plot(context['ry_hat'], label='predicted')\n",
    "ax2.set_title('ry')\n",
    "\n",
    "handles, labels = ax1.get_legend_handles_labels()\n",
    "fig.legend(handles, labels, ncols=2, loc='upper right')\n",
    "plt.tight_layout()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a70e93e3-be2a-4893-b6fa-c1aa374232b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aQgjRxDweDxkZGdhsNpncQYgjjPPgQfK++QaAqKuurDatGh6GOycHjwQzooVrUXeqtm3bAvgDGiGEEE1PVVU6dOggFUpCHGFyP/0UdB3r0GMIPf74atMaYmJx7t2HIyUF27HHNlEOhai7FhXMKIpCu3btaNOmDU6ns7mzI4QQRyWTyYQa5NkTQojWS3e7yZn7KQDRV11VY3pjYiIl69bhzsxs7KwJcVhaVDDjo2ma9NUWQgghhGggpZs24c7ORg0PJ2zs2BrTG2JiAHBlZjV21oQ4LFL1JoQQQghxBNN1nfQZMwEIGTkSpRbj4QyxZcFMtgQzomWTYEYIIYQQ4gjmTEmhePVqACIvvKBW62gxsQC4pWVGtHASzAghhBBCHMHsO3YAYGzfntBRo2q1jr9lJkuCGdGySTAjhBBCCHEEc+zcBYClf79ar+MfMyPBjGjhJJgRQgghhDiC2XftBMDctVut1/F3M8vORne7GyVfQjQECWaEEEIIIY5gvpYZc9cutV7HEB3l/Y/Hgzs3txFyJUTDkGBGCCGEEOIIpXs8/jEzpq5da72eYjSiRUYCMj2zaNkkmBFCCCGEOEI59u7FU1iIYjZj7lL7lhkArWwSAHeWPDhTtFwSzAghhBBCHKFK//4HAEvv3rV6vkx5hrJxMzIJgGjJJJgRQgghhDhClf69CQBL//51Xtc/o5l0MxMtmAQzQgghhBBHIF3XKfp9JQDWfn3rvL50MxOtgQQzQgghhBBHoOJVq7Bv345isxF68sl1Xt/fzUxaZkQLJsGMEEIIIcQRKH/RDwBEnH02WkREndc3xMqDM0XLJ8GMEEIIIcQRqPDX5QCEnTq2XutrvjEz0s1MtGASzAghhBBCHGHcubm4DqQCYB04sF7b8HUzc0s3M9GCSTAjhBBCCHGEKd26DQBjYiJaeHi9tuHvZpadje7xNFjehGhIEswIIYQQQhxBdF0ne/ZsAMy9etV7O75uZrhcuPPyGiJrQjQ4CWaEEEIIIY4g2bPnULh0KVC/KZl9VJMJtaxVxy2TAIgWSoIZIYQQQogjhO7x+FtlDO3aEXHhhYe1PU9+PgD2HTsOO29CNAYJZoQQQgghjhDOlBRcGRkoRiPdfliEsU2bBtluyt33NMh2hGhoEswIIYQQQhwh7Fu3AmDq3g3FZGrm3AjR+CSYEUIIIYQ4Qth37wbA3KVrg2yv06dzAVCMRpnRTLRIEswIIYQQQhwhHHv3AmDq2LFBtmfp0wc0Dd3pxJUhD88ULU+dgpnp06dz7LHHEhYWRps2bTjvvPPYWtacKYQQQgghmo+u65SsXQeAuVvDtMwoRiOGeO+4G1fqgQbZphANqU7BzLJly7j99ttZtWoVP/30E06nk9NOO42ioqLGyp8QQgghhKgFx44dOHbvRjGbCRk1qsG2a0xIAMB5QIIZ0fIY6pJ40aJFAa/nzJlDmzZtWLt2LSeeeGKDZky0bp7iYhx79+IpKcE2ZEhzZ0cIIYQ4ohUuW8b+m28BwDpwIFpoaINt25iQQAlrcexPbrBtCtFQDmvMTF7Z02Cjo6ODvm+328nPzw/4E0eHrUOOYff5F7D3iisp+OWX5s6OEKICV06OPNG7Cbjz8ylYsgR3fj6uTBlvIBqPL5ABUAx1qquukW53AJAxYwa6rjfotoU4XPUOZjweD3fffTfHH388/fr1C5pm+vTpRERE+P+SkpLqndHWyvejDzYDiKe0tMqLgn37dg5Ofxp3YevrwufYsyfgdcEPPzRPRhqQ7nKxY9w4Nvfrj+52N+i2XTk5ZH/wIQU//9yg2xUiGF3X2TJgINtHjGTb8OMo3bqtubN0xNI9HnaefgbJt97GtmHD2X7CKLYOOYb0557DsW9f0+fH7ca+c6cURo8C8f+b0qDbCz/rTP//7Zs3N+i2hThc9Q5mbr/9dv7++2/mzp1bZZopU6aQl5fn/9u/f399d9eiFPzyC8l331PjFIWe0lJ2nXU2m3v1ZkufvhQsXowjOQVd1yleu5atgwazpXcf9l51dcB67sJCdo0/h+z33mPb0KGN+VEaXPIdd7Lz9DMClrnzC5opNw3HsW8fzr37wOUi7YknAHBlZvpnjTkc20eM5OCTT5I88Q5y5n562NtrTTylpRSvW4eu6+huN7lffcXB/3sW3eVq7qwdsYpXrkR3OPyvd597bjPm5si259LLcGdnByzzFBeT9fY77DxtXJPnJ2PmTHaddTZZb72N7vHINLtHkKzZc/z/7/zlF5i7dWvQ7YefdhrmPr0B2H3BhQ26bSEOV72CmYkTJ7JgwQKWLFlC+/btq0xnNpsJDw8P+GutPEVFuPPz0XWd5Ntup2DRIrb06YunuLjKdezbt+PYtcv/Ovn2iewcO5YtAway98qr/MuL16yhcNky/+vy7wFsP+UUf6HZlZVF6tRplP77b0N9tAZV8NNPlZaV/vNPM+Sk4TgPprPrzLP8r3M/mcvmXr3ZfsIodo47nfxFP5D68MM49uyhdNs2NvfqzZ7Lr8Cdm1vjtj2lpQGv06ZNY/ell1Ly118AFP/5J5lvvYWnpOSwPkPJX39RuGwZ+/5zPSn3P9CsNbN7r53A5l692XfjTWwdNJi9V1xJ9rvvsm34caROnkL2u++S+fobDbIvj8PhL7B5SkrQXS50p7NBtt3YdLe70vlRk9LNm9ncqzfps2aROnUaOXPnej+z203ma69RsunvoC0xroyMhsp2k0ifMZPNvXqT+8WXActL/vmH7SeeRNbsOehOZ7Oe557SUko3bao2TfI995BfNha1eN16SjdvRne7caalUbRqtT//npIScuZ+Wq/Kk7xvv2Vzr96kPfY4WW+9DUDGCy+wpU9fto0YKQHNEcB58CDpzzzjf23u3r1R9mNKTDy0z9TURtmHEPWh6HW42uu6zh133MH8+fNZunQp3ev4g8nPzyciIoK8vLxWFdjoTidb+g/wvlAUKHfIIs49h4RyFxGAvK+/xuNwkDHrRdx16CNt7tnT/+TeqoSecgqFZWNQui76HsViwb59B7ahx6BarbXeV0PwFBWx88yzcB08SPtXXsY2/LiAlqTEmTNIufseACIuupD4yZPJeucdwk45Bd3hwNSlC4aoKMDbGoXLhRYZ2aSfoTa2jzqxVoU9y4ABlJYFIT69t1TfHJ98x51BA0AA2/DhFK9eDUDomDEULl6MFhdLj19/9daqulwoqlpt3+iCxYtJvn1ipeXtX30VT0E+hrbtKFm3Fi0qivyF35P01puoZnNNH7XeXDk5bB8xslZp2/z3v0Rfew2KWvs6F1dWFjmffkroSSex58KL/Mu7LVvKjpNODkjb6+9NQY+dfdduUu6+G/uOHbS5/34iL7rQ+xsbMrjW+Thc+T/+SMqdd6FYLMTefhsZz79AuyefxHbsUPbfdDOOPXsIHT2auLvuxLF/P+GnnoojOZmdY0+t1/7av/YqYaNHA2DfuZOMl14m/v77MJYrvLQUntJStg4K/C7av/4axoQEdp9TuZWppt/g4SpatZrk227D0r8/Sa+9imqzAVC8dq2/Yipk1CiKfv21ym10+X4hu8441I0HoxGcThKefw5Ljx7sGn8OAIaEdtiGDiVs9GjCz/C2gBetXIl91y6ir7yy0nadBw9WOu8r6rroe0ydOtXhE4uWZuuQYwIqVhvrnC9/jUmcOZPw05u+dVEceRoiNqhTMHPbbbfx8ccf8/XXX9OzZ0//8oiICKy1KEi3pmDGXVjIjhNPwlNcjO244yhetarKtNZBg2j76KO40tNxZWWSOrlh+6rWVrfly8Dl8k+h2Fh0l4v9N91E0e8rA5abe/TAvs1b69vl228wd+/O5l69q9yOYrXSa/06PHY7WwcOAiDh2WeJGH92w+ZX13EdOIAhIQHdbgdVRTWZalyv+M8/yf74Ywq+X1Rj2qqYu3cj7q67CBs7ttJ7uV98SeqDD9Z5m91X/u4PCNTwcHr+sbrKtNUd/2BCRo4g6qqrCR19Moqi1DlvNSndupXd555X6/Rt7ruXmBtuqHX6bSeMqlMFQrCAJuOll8l85ZVKaTvMfpeQESOq3JYrO5vsd9/FNnw4oYc5JWpdv7eI884j76uv6rRO2BmnY/93s7+2v9dfG1FMpoB991jzJxkzZmLfuZOE/3sGY5s2ddpHQ9JdLnacehquOtYIx/9vCtHXXNOwefF48BQUoBgMbD3mUAVO+JlnYO7VG0NMNK6MDDJmzgIOnWf2Xbsp+v13DpZ1VfWJuuoqcj78sNJ+bMOH487Owr59R6X3FKMxoJXR0LYt3Zcu8b/O/+knUu64s1afxzpoECEnjkILjyD6qiu9rYIlpWihIbVaX1Sm63qjXEOD2Xn6Gd6xqopC782N22vjwOQp/mtNY1cUiKNDkwczVf0wZ8+ezYQJE2pcv7UEM+7cXLYdV3Whpa5ibryR2Im3k/PxJzj27CH3U++4CDU0lC7fLQhac2ZMSiLpzTcCa+tqQTGb6frDIrI/+IDsd97FEB9Ph3fepnj9esLGjkULD6dg8WJcB9MJG3davQon6bNmkfXa69Wm8V3kUh9+mNzP51WZLvLiiyq9bx04kA6z3/XXcFbFmZKCMz0da79+KEajf7mnuJj9t96GpXdvbMcNJ/mWWyut23PtGu+4DF3HXVhE2tSpFK1YUe3+zH16k/Taa/7vyzJwAKUb/6p2HZ9e//yNommAtyC0pU/fgPcTnnma0m3byH7n3VptrzzbsGHeGuGQwIKH88ABdpwyps7bA4ibNImYG66vU6uIruu40jMwlj1cTdd1XKmpGNq1Q1EUdF0n5/33OTj96Wq3o8XF4i73lOmqbpj7b7mVwqVLSXjuOQ7cd1+t81me7bjj6DhnNq6sLLYff0KN6Tt//RWKqlbqxuHOy2Pb8OP8r3v9tRGMxnoVZnRdZ0vvPnVer65ib7uVvG8X4Cw3ljHu3klkPP9C0PTR111H/H8fqHabuq7jKSpCDQmp8rO7CwtB18n5ZC6mDkm48/Mp/uNP2j06DcVoRDGZ0D0e/7nnysgg5Z5JFK9ZU89P6hUyciSl27eR+Mwz2PfsIeryy+td2Mx48SUyX3211ukrnsNFq1azrxb3zLqKf/ghoq64AndWFttPCB5QW485hpK1a6veiKpCua5nYaeOxb5tO52++EKCmxrkL1xIyqR7/a99lQxRV1xO1JVXokVH+3sjNJTy51KnL+Zh7du3+hUOU/nKHmNSEl1/WFSn+0RTKv33X4pWriLm+v80d1a8PSkaeIa5I0WTBzOHq7UEM7nz5pH60MPVpmn76KMcfOYZ9GrGzABEXXEFbR+pvC3d6UR3OFBDQthz5VUBNxdTly50eOdtjO3aAVCyYQPuwiKSb721Vv39tejoSoNOqxIyciSmzp1p+/BDActdOTk49+3DMmAA7pwctKgob4HU7Wb3BRdW2x0u5sYbaHPvoQu6r8tMXYSMHImlXz/i7pgYEKj46Lru7fpXNlC8w/vvkff11+R98SWGhHa4DjR8f96uP/6AqUMHdI+HwuXLsQ0diicvjx1jKre6tH38MdIefsT/2jJwANZ+/cn56CNMnTvj2L07IH3vLZvxOBykPTKV4j/+CHgwWfj48eR/+22N+ev2y2J/q5zu8ZD60MPkfXloTEHE+edjbJ8Ibk+dCmJx99xD+OnjMLZvj6JppD//Arnz59N10SK00JBK368WEUHXxT+T98UXHJz+NG3uv5/0Z5+ttN2ef20k6+23yXzxJf+y+CmTibr66oBgr/M3X2Pp0cP/+uDTz5A9Z06t81+R7dhjKf7zz3qvD2BMTKTTZ59i37kTc+fOFCxeTNq0Ryul67FqJVpkJB67HU9xMa6MDA7cdz9RV1xB0apVhJ0ymtCTTkKLiPCvU757Un3E3nYb0ddN8J5Dbjembt3YOmBgQBrbsGG0f/VVcLsCgrBqGQxYevcm+pqriRg/Hl3XKfj5Z6z9+7Pj5NGVkvdcu4ailStJnnhHvT9LdYJ16wToNPcTChYv9o8PqU7HDz/AFmSSlYq16uW7R0Zeeqm/Qqo2Is4/n4TpTwVu3+FgS4XvxMfUpUvAWEufmrqq1cQQH0/YuNOImTCB4jVrCDnhBLaPPL7W64eNG0fi889h37YNc69eAQXYkr/+wpWZRdgp3vMg98v5pP7vf6ihoXSYPRtj+0QcO3di6tiRgiVLiDj7bIp+/x01NJSQ447DU1xM+syZKAYjJevWoZhMtHvicfZcdjnxU6YQMf5s8r7+mgP/nUzSW2/Wu+VTd7nIeuddTF06k/bYY5iSOmAdPIjilavo8P77lYI1Z1oaWlhYpYqiqtSmRbX9q69iHTQQQ4VHWugeD+7sbLSYGDx5eQFdrt0FBSia5q/gK/xtBY49eyj48UeK//jDn67bsmX+yqTGEtDtntq1nuseD7rdTtY77+I6eJC2Dz+EUtY7omTTJgwxMQ3eo8R3voC3ItI2eAixt92KISamVuvn//gj7uwcoi679LDy4crJIe/L+f57YM+NG+rclTv7449Jf+b/6PzlFyhGI/kLF2Lp3ZvQk04CvBW4mW+9hSEqCmfKAeLuurPGyuCWRIKZRpK34LugNb1df/oRT0EBhnbtvCdNNf2Ro665mpCRIwkdNcpfI18VXdfx5OVRuHw5JRv/Iv5/U4Ku40xJwb5zJyGjRnkDis2baf/yS6g2G/v+c329PqtPp0/nosXEkP/dQtzZ2ThSkin8eXFAGlPnzrgLCgK68YSMGkWbSfew+/wLAG9BtvuqlZVqPNMef4KcTz6hzaR7yP/hxxoHxvrE3HgjGDSir7gCQ1ycf7kzLS1oAaoxWAcNosP771XbNa18YSdYgbw67V95mbAxh1pQym8r9vbbibtjIiX//EPWm2/VOM119IQJqOFhAQFC28ceJeqSSwLS6bpO2qOPkjv3U7TYWLr/urxerQEd5sypVw1z+Vp+XddJf+45FIORNvfc7V3mdrOl76Ep39XwcCLPPw9zr96kTqm+G2fUlVeihoQQd+cdKAaDv4Ch2Gz0XLUSxWSiZONG9lx6WbXbafv4Y6Q/+xyew3g+ljExEWdKSo3pIi++CNtxx5H50ssBU5v3+nsTGS++BLpO1ltvAd7fXPuZM0i+445KXT1tw4bR8f33Km3fvns3jl27CDnhBHSHAy0szP9ewZIlJN96W50+V8IzT/sLCs3BFyA4D6ajRYSTMWMG5h49iRh/trdlp+y2VptzOubGG7ENG4Z1QH/SZ84k9xPvDJ2xt93mD/rj7rqTjFkvVrmNuEmTyHgheItWVQVMd2ERrvR0dp15qPW9w5zZhBx3HLrTye5LLg2YArf7b79SsGRJQCUJQMS551K6fRv2f4O3YHb85GNMSUkYYmMrvZf9wYccfPLJKj9XdXyVFKaOHXFlZeEpLCTkpBMpWra8xnWNSUkBLYJ1ZR0yhLg77yDkuOoD8eyPPqJk7TqiJ1zLnkuqL5QqFgu9Nqz3vy7fDThk5EiKfv8d8LZ+KZoBS9++WPt7r1HuvDwOTJ5C4ZIllTdchU5zP8E6aBAehwNPURE5H39M5ksv+9+PvvZast879FvWIiNrnFSmqbp95X33HQfuPVRGSpw1i7BTRqMYjbgLi8j58EMizjuXgl9+wdqnD+mzZlG8MrCrvhoSQvj4s8ktm8HT2LEDHd//IOC34i4sIm/+fHSnk/Azz0DRtIByQHm6rpP+9NNoMbFV/hYBeq5fFzC+2FdxsWXwEPSSEkJPPpnQ0aNJmzoVONQF1DpoEIkvPF9t0FW6dRuujAy0iAj2XHxxlekiL72UyIsvRi8pRg0Px1NQgBYVReHSpUT/5z8B5SfnwXR2lAUtFZW/RpUXdfXVxN58E1pMTJN1dTwcEsw0krxvF3Dg/vuxHXcc7V9+ucam9WDdQrr/9mvQm0dDcefl4TxwAEtvb0Ft38031+om0pCsgwfT4Z23UW029lx1FSVr1tJtyS/+FqXq6LrO3ssup2TjRsA7QUCbe+9l37UT/ONuAhgM9P77UACUfNfddXp+TZcF35I27dEau6rE3TsJPDqFy5fjSk2ly4Jva13DUbE2V3e7cezdGzATWnlaZCQ9Vq0M+l5V9l53HcUrVxE6ejTtZ80kZ948Dj72eLXr9PhjNVoVv7fy3XnAO+4j9/N5ZMyYUad81VX5VqSqlO+bXRuK2YwxqT1dvv024HsoWLqUzJdfIfH55zB17OhfXl1XofYvv0TY2LHobjc5H3+CbdiwWk1hHHXF5eR8/Emt81wdLTqaHr8f6vpYvHYtpf9uJuqKy1E0DY/DgWPnThSTiZR77yPivHOJqWfXpYwXX6Jo5UpK1q8n4oILaPvg/0i+5x6Klv9K2LhxuNLTKVm/vuYNNSI1IgJjfDzR111H2JhTqjynyyvdupWCxYsDgvvG0O2XxRT8ssQ/FqbTF/Nw7k8mZMRxAa1uwdh37SLvq6+Jvf22gBpbXdfJ//Zb8r7+BkufPrS5d5L/vYPPPkvx6j/oMGc2Wmgo9h072HvtBNxZWZW232vzv9UWaDwOBwrgTE+nePVqUh881EpfVWGppdDiYgk59lgcKSnEXH894aedhu50cvCZ/ws6BqkmcZMmYerYkeK1a8h5/4Ma0/vGQlVskTG0bYsrLa3O+68vY1ISXX/8oUkLriX//BMwyUrYqWMp+Onwn5UWcsIJxE28nT2XXV7pPdVmo8v3CzHGxwcs191u7Dt21nqaeevAgURefBGmTp3Ye9XVGNq0wZWeXue8Jjz/HOYuXbD07l1ta2td9Vi1EkdyCnsuuqhSt+u6qun33xJIMNNIcr/6itTJUwg5/ng6vFNzVwUf3e2usRWmsW07/gT/Da3X5n/RS0tJvvMuVIvFP2tWuyceJ+KCC9gx+hRcBw/Waz/WIUPo+OEHh9VX1lNSgu50ViqUVNVU3+2XxRjatWP7qBNrHOQdftZZWPr0Jvu99+n8xbyA2pycuXNJm/YoitmMbrcTddVVmDokEXrKKZjKphrX3W5QlAbpC5z55lv+mqL2r75KwU8/kTd/fr0GJnuKiylatZrQk070n2vuggK2HTusynXqWlun6zp7L78C3eWi9O+/a72eFhtb7fdi7NgBS6/exP9vSqWbUdB8VGidKS9k5AhMXboSc/1/KFy2nJCRI9AiIlAMhlp3CQG8NZ8GA/YtWyj+4086fTEPLTQ0aNrSrdvI+/ILCpf/WqmboE+XBd+S9fY7dR6MH0zXn3/yn4/NrXDFCvZfX/uJGCoy9+iBY98+bMccQ9GqVXT75RcMbeLA7aZw+XLU0FD2XX8DIcOGUbxuHda+fdGio7Hv3En7l16kaNUqoi65pN59zn3TDyuq6n220erVAU9Lr62QE06gZMMGPIWFJM6aBbqO7nYRcdZZh/bj8TRb3/iU++4nf8EC2r/yMuaevVDNpiprsoPRdZ0D9z9A2GmnEn7aad5lFboV1Vb5mTebSl3GMQLeVrxyz1yqq5ibbybygvPZOe70gOW9Nnnz4OsinfvFF+R9861/dsqGVp+uSw2hYhf5pmKIi6Pt4495u+pbrey/8aag6awDB/orTI92VXWpbQkkmGkkvublkJNOpMMbDfO8i6bicTjIfu89oi6/vFKhLPOttyj96y8SZ80KKKRXFTzE3nYrma++FvS98gPaG5q7oIDM116ncMmSKguNPnGTJhF70424CwtJvuVW2kyejKVvnxZVE6HrOiXrN2Dp3cvfvO3KzGzQJuDC31awv0K/5e4rfkOLjj6sfZT++y8l//xDyYYNlG76G2daGp78fCIuvIC8cs/46LZsqXfQt9FI9uw5eOylRF10EbrT6X+IaszNN/u7kdVW0arVFPz4A/ZduwNmFKw4jqap+Vpvy+v81XwsvXr5X+849TR/V5qoa67G0rsPlj59MCa0I/355yn56y+SXn+dHSdW7kIQe8dE4m6/vXE/RB2lPvwIjr17/X30zT160OWbrwFvdxBPUSHJt95G+Nln4ykpRjGaMHft4v3MZa21usvlLewH6bLpLixEtdnwFBWhmM21mnHwcO2+9NJKhd/wM88kf+HCoOlbQy1nYyj45ReSb7sdDAasAwZQsm4d4O1W7M7Lq5S+07x5WHr1pHDZMpzJyYSefDLgHUvj3L/ff3wjzjsPV0YGMTfdRMhwb4WM7vHgKS5m15lnEXf33VgHDiD3s8/Jfu89bCOOo2TjXzWOVa2WwUDHD97H3L0HqtnkfU7YI4+g1/QsL4MBS8+e1T43LeaWm4m67DKMbdsGfd+ZkkLWe+/h2LGjUhfRYCwDBxBx1lnkf7fQXyjv+OEHWHr3pmTTJvZNuA4tNpYev9V/LNXhyH7/fQ4+Nb3W6bstXULp5s2VurV2/ubroNOqHw5fZbQ7L49d559P6Ekn+buQ1kb4+PF4CgooXLr0sPJh6duXTvM+R1EUnGlpFCxezMHHn6h5xSrEP/QQ4WedWekRB8bERLr+sIiMmTPJevudoOvWdXbQpiLBTCPJ+fxz0h5+hNDRo0l6reU2sTcU3+dNnPGC/9kFPs6UFDAYcOzahSEursGfKlyTzNdew7E/OWAgu0/U1VfT9sH/NWl+WrKSv/7yPhdEVVHN5hYzADD7/fcpWrWaxJkz6l1A1Z1O3IWFDT4T0OEo/G0FhtgYLL16BZ2G1Rdg1nSeekpK2Dp4CACJLzxP6JgxzVLLWlu7xp+Dffv2oGOxWht///5zxqN7vLdCU/vEgBkH1bAwPAUF2EYcR8fZs5szu83KU1SEx26vNHDdp2j1H+y79lqi//Mf4h+4P2gaH3dhIaX//Ivt2KF1bv12JCfj3LfPP07U0K5dtdN1h550kv+h1D1WrUR3uSp1AQ/27CKfDnNmU7JhIzE33YiiquR89hk5H3+CfcuWgHSmrl3p+t2CWn0G3eGgaPUfGBMT2Hfdf4i97TYMsTGYOnSo8oGXuq6D0xlQEaA7HPWeNbEh6C4XGbNmkf3+B97HHgBhp56KsUMS9q3b0F0urP37kfXW27R7ejqR550XsL47Lw/FYkE1m9F1ndQHHwq417d7ejqGqCj233pbwAx71Ym85BK0iHBiJ04MuI668/PZNmw4UHlac5+w00/HnZdL/AMP+LvwO/bu9VYKGgy48wv841dMXbvi2Lmz0jY6zH6X3M8/J3z8eCy9vBWYFZ+fZ9+5k8IlSwgbNw7Hvn1Y+/dn98UXE3n++f7p3Cvq+OEHqKGhmHv2RFEU3Hl5ZLz8ineCAkVBtVr9lUYFS5dSvGp1lRPlRF15ZZVjs5uDBDONJGfup6RNm0bo2DEkvfxyzSuIRhVssH+7J58k8sILmilHQjQs3ePBlZHZ6DMRNQRnejrFf/zpHZDbQqdkbUiuzExyPplL7K23yNSqLYh9126cKcn+mc3K9zCwDj2GpNde8090Yd+1Gy0qssbKEHdeHvk//kjkeecFnUWzvO0nnhQwzqLD++8RMqzq7r5HsvJdgqt6GHGdtleuW6jvdenmzShGY5UtOPFTJlOw+BeS3ni9ygeIu7Ky0N1u/yMp3Hl5/jFttX0ukKe0FHQdT0kJ9q1bUYxGDk5/mg7vvlPj+LjaKPrjDzwFBSTfPhHrwIEkzprpfch4hw513lZNs+v1/Gtjk7SA10SCmUaS/fHHHHzsccJOO432LwaPkkXTSr7jTgqWLsXcyTuAu8OcObWeYlEIIcSRrXjNGlLuf4C2Dz9E2CmnNMn+DjzwX+LuvouIc85p9P0Jr9LNm1FDQ8n97HPCTjsVLSoKY5s2QbuuHu1cOTmUbNyIISaW9GeeCToBUs8N61EtlmbI3SESzDQS33SVYWecTvtGntVJ1I7ucOApKWmQmg8hhBBCiKNJsGe0Nff4U2iY2ODI7yNQHx43AIraMvoTCu+sMxLICCGEEELUXfzk/9Jt2VK6fPuNf9mR0nVWgpkgdHfZQDNNDo8QQgghhGj9jPHxmLt3PzQpQS0nVmjpjoyQrB50XSfrzbeCTv1r374dkJYZIYQQQghxhPFNdtB0I00a1VEbzNi3ba/xKedaC5oGVgghhBBCiMPmnylOgplWzVNcBHgf+hVz042V3lfMFsLPOrOpsyWEEEIIIUTjUX0tM9LNrHUr6yeoRUYSc/31zZwZIYQQQgghGp+ilI0JP0K6mR21I9x1l3fGMo6QmRyEEEIIIYSoUdmYGf0ImQDgqA1mDk2/fPQeAiGEEEIIcZTxlX2PkDEzR21JXlpmhBBCCCHE0UbxzWaGBDOtm69lRpPpl4UQQgghxFHCF8wcId3MjtpmCd1d1jIjD8YUQgghhBBHidDRo3FnZ6NFRDR3VhrE0RvMuFwAKNpRewiEEEIIIcRRpu1DDzZ3FhrU0VuSL2taW703l5MnfwfAlcM78NHqfVw+LIlP/tjvT3rP2B7M+HkbUTYjOcXOoJubcelA7vl0Iyf3jGPp1gz+d2Yvnlq4JSBNl7gQdmUUVVr3rP7t+G5TKucMTOCbjQcC3ju2UxR/7smptM7onnEs2Zrhfz3rskHcNXcDCREWsooc2F3ez9e9TSjb0wsBuO74Tsxesce/zvMXD+Tezzfy0uWDueOT9QzvHM3q3dlcOKQ9X6xLBuCUXm34ZUu6fx2rUaPE6fa/PqlHHMu2ZTCqeyy/bs8MemxUJXCMmS/tBUMS+XJdCnFhZjIK7LSLsJCaVxqw7qRTe/DCT9u4+aQuvLFsF5cP68Anf+xj8hm9ePp77/HtGGMjOaeEuFAz+aVO2kdZ2Xaw0P+9+dx2cldeXbqTq4/ryDcbD+Dx6BTYXQH7u2Roez5bk8yYXm1YvCWdCKuRvJJD33m4xUB+aeA6MSEmsoocjO0dz8+bD/qX/9+FA3jgi7+44YTOvP3bbnrGh7H1YAHTxvdh2rf/ctaAdnz3V2rQY3bNiI68v3Kv/7XvswzuEMn6fbn+5QkRFg7kldImzEyP+DB+25FJ+ygrqXmluMsOuu/4+tx3Wg+e+/HQcSn/nfg8e9EA7p/3F0M6RLJuX67/ONw+uiuvLNnpT+c7333fJYDZoPrPP4Arhnfg49X7uOnELry5fFelYx3suJ7Rry3f/53Gq1cO4baP1nHVcR34cNU+/3dY/ryuyWXHJjH3z/0By8b2bsPPm9OJtBnJLXb68+Y7n32O7xbDih1ZAJWuC4+c3YfHFvzLnad048VfdviXT7+gP1O+3ARA59gQdmdW/s3fNaY7sxZvJzHSSkpuCY+d25dHvv6HYzpGkZpbwoGy30GISaN/+whW7crm8fP68fBXf/u3MXF0N15esoMXLhnIpM82VnsMyv+my/P9vn3f442jOvPWr7uDbmNElxhW7sryn78XDE7ky/UpTDmjF9PLfovljxd4z72CUid3jenBM4u2+L+L+8f15NkftnLuoAS+3nAg6P7+76IBPDDvL/91+ewB7VhQ4ffSrU0oO9IL6RIbwq5yx9n3W/Sdt77rhsWo0iHaxraDhUw6tQdWo8ZfKXl8u/GA/3s8rU88P/57kP+e3otnFm3xr+szrm88P/zj/Z1X/N34vlffd+MzKCmSDftzA/Ju0lQcbk/Q7ybSZsTj0Stda87s35aFm9KCHi84dL33HbOqfH7LCC5+faX/dde4EHZmFFV7TfL9Jod1juaP3dn+5RXPS59ubULpEG3jly3pTBjZiTm/7/G/9+g5fZn6zT8MTIpk4/5cesSHsu1gof9aoShwYnfvb9F33UyIsJBZ6OCCIYnM/XO//37quxf4zmHf9cd37QL8162K1xnffRPAoCokRFpJLygl2maiU2wIv+/MCthO+ePwn+M78+6K3Zw3KIGvNhwIOOa924WzOTXfv47veu67jvnu+def0Jl3fvP+3kJMGkWOQ/dWX14/ufE4Ln9rlf/8v+iY9sxbmxzwWXyf3Wf9w6cSFWIK+j2Wt3BTKrd9tK7GdL58+vY9rFM0f+zJDiiH+K4lfdqF829qPneO6c6Li7cH/H5MmkqkzUh62f3oyfP78eD8v/3nVK+2YWxJK/CXp8rfVwD/tgH/79T3m3jj6mO4+YO1/vMlmAfP7M2TCzfTPzGCTSl5/mu6L68+vnt2eeMHJvDtxgP+79Z3b/Itv/iY9ny+NpnYUDOZhfaKuwbgqfP787/5m/zXUt+1pPxvquJvxXfP85W9fOeB71oTjC9NxXuT75oG+MsSO586E833zJlWTNH1pptkOj8/n4iICPLy8ggPD2+q3QaVt+A7Dtx3HxtiuzHlhFuaNS9CCCGEEA3BbFDZ+sQZNabrVFaRK45ek8/oxS0ndW3WPDREbHDUtszYBg/imWOuIM8c2txZEUIIIYRoEOVbxoWoTnJOcXNnoUEctcGMMTGRpUlDmjsbQgghhBBCiHqSqbyEEEIIIYQQrZIEM0IIIYQQQohWSYIZIYQQQgghRKskwYwQQgghhBCiVZJgRgghhBBCCNEqSTAjhBBCCCGEaJUkmBFBmeJ+wBT3Q3NnQwghhGh2ijEbY9TvoDgbdrtaYYNvUzQHD6a4H9FsO5pul6odxZDfdPtrwSSYaSWM0csxxSxp9P0Ywv5Cs+3EHLsEc+wSUFvwA5UUJ6A3dy5qTTFlYI7/CsWQ29xZqZK57ZdYO7wFlD10TXGhWfcA7nKp3JVXBIxRv2FJ+PjQuq2B4kQ1pTfxPh1Nu79G48EQ+o/cTEWjUK170UK2A66yv9pTTBkoxqwGzI1OSJcXsLT9BnPcT+WWuzice5CiFRDa4wlCu02v44qOw9qvqEwx5IJSt/OsPGPEWsyxv2Dr+HbDZaoGoT0eJbT7UyhaQZPts6WSYKYcQ9gmQro+g2pJrvxmc9acKE4s8Qsxt/kBQ/h6LO0+BbWk9qsb8oN+JsWQg7X9HH9NgmrZh7X9x9g6vnUojVL/gqmiFQQt6GghW7F1fA3FlFG/DWtFKFohoT2nYU2aU3N6tQRD2MYGKEQe3o3L1vF1TNGrsCa9d5j5ANBRLftALaUhggfNugct9F9MUX9gCNmJZt0HgCXhM2ydXscU9yPgPWdCez6Cue18/7qKMRO0IixtF2CM+AtD2D+HnZ9DG3dgjPzDe24mvdvgtV62Ti8T0vWFskJTQ9ExRv2Gat1b6R1z/NeE9XrE+921KhXOe60IY+SfWJM+IKTL85WTK846XaMaVSNcuzXbDrTQzbVKq5oPeH8jjU0todbXArUUS7tPMYRtwtxuHlrI1gbNimLMLLs21V9Ip9ewdXiHsN4PEdJ9OlrINsxtv6LGz6g4CO36PKHdnqWqipdDdMzxX2GMXF1lCkP4ekK6P4miegu6mm2ndzdaIaE9HsOSMBff70O17MMY+Qe1vU9otj3ebRlqX2moGHII6/UI1qTZtV4HgJy98PXtkF7Neas4aqjA1NGse7ytSb5VtMKqK4QCfnsubJ1ewtLu8zplu0qqvayizXc+eDCE/VWhstBDbb4L1ZJCaPensXWe5V2gOLEkfoQx4s9aZ0cxZdc6bTBayPZa398MYX+hmtP85TPVGli+U0wZmNssPKqCHENzZ6Alsbb/qOzf9yna8T//csWUQWjX53HkDsWeelEz5OzQj9Ga+Kl3iW7Cnnb+oSRqKdb27+Mq6I8zZ0TA2qHdnwKgaNfdeOxt/cvNbb/FELYFQ9gWnLlD/AXYhuEmtMeTABRseQL0Q6earYP3ImxNmEtJ8rXorvAqt6LZdqKF7MCRMRbQMESswZowD48zDEVxYwit+SZsbf8BhpBd5b4/N8bo33AXd8VjjyvLm1KWOnh8r2gFhHR7BldBP0oPXAaAuc0CdI8Zd1FXtNBtODLHBnzOilRDkfczWVIrv2fdi+4MR3dF1fh5AAzhf2FN/ATdbUXXDbjyBmJPP7vG9VRTOh5XKHhsActtnV4P3H7EWsxt56NZDgJgivkVR8YZmGJ+RVHdmKJWY087H8WQS2i359B15dDK6uG3PKiWZAyh/6IYijBFHSpoGEK3UbD56eDrWPdiCP237Hsw1mo/vs9nDF+Pu6h7FXnZh6J4cJd0qn5jigPNuh/FUICl7QKASnk1Ra8EwBz3EyX7r69VHpuGC0P4P7iLuqC7wwLesSR8impOo3j37YABzbon4HxRNHu51N6ba2j3J1G0Ugq2PYyCp9I2QccQ+i9uezt0Z3TDfhTFAboJ8BZErYmfUpp6Ps7c4d5lEWvBY8JV0N+b3JiNohXiKe1Qyx3o/trXwu3/q/b6hVpKSJcXASjY/BSHW3+oWXdhSfiC0rRzcRf18C9XjFmEdnsWV2F3SvZPKNuPUtVmMMf+gjFyPcbI9QCYItfgLknEmXcMzpyRdc+YYi+79mn++6XuMVG49bG6bysI1VCErcO7AHgcMTizRx16z5yGKXo59swx6M4YFK1cYVy1g8fCoePuQTVl4nHEAQqabQem6FUAuAr6gOJBNaeiu0PxlLYHDt1zKzJGrEXRHBgjNqLZ9uAq7IEpylv49TgjA76fgM9i2YfujCr7TdS9cswYuRbwXgvr5NMrIW0TbPkO/rsnaJLQHo+jqE4Ktk4FjxUtZCummGWUpp2PMWId6CrmuMXoHiOFWx8vW+cJAEpSLkez7caefhp4bBjCNmJt/wmlqRfgzB2GFroDzZqCZk2hNPXisj16yr4jq/ezRfyJKe5nSvZf5y+rqJZkdGckujs0IK+2Dm+Wbcu7fWPkn1jazUfXVQq3PAWKg5Cuz+Mu6UhpyhXebZlTMYRuxpE9KuAeYQzfCIBmzvBWoEVswBi+CWP4Jpx5xwYeJMWBFrILd1HXQ9tQDq+iE9WOrcM7ABRseRx0I4pWgGrKqnTf0Ww7sLb/uMIGAn/rIZ1fQlEdqOY0Svb/p/75akUkmAlCqdDUaIpZ5v03co0/mDFGrUB3ReAq6FeHLeuguKst8Fa5XgWqMbAWwBT9K4aQXRhCdlUKZnw0676AYEY1HIrajZHr6pinGqiHCjiKWhKkMAOaNYXQ7k9RevCsgJtTeb5WIkUrRjWlYwjZ7d28sfY1DoaQXcCh788Y+SeW+O/973ucEaAb0HWN4l33AAqG8A2Yon+lJOVydGcsxsg/UFQXxogNlB64DMWYjSnmN+8G4hYDoLttOLNPrHW+fFRzGiGdXgMqF36rYgz3FkIUrQQFMMX8hiP7RFRjdpWFbtWcRkiXmQE3ItWUHrT1zHdTPkSp8K+XVlYjpCjlz1G97K/qwlRNbB3eRtGC1+ya4hbhLuqGu7hbwHLfMUQ34Mg81dtaFL8AZ+5Q3MVdAW9BVjVl4bG3Qa1Vy6CbkM6vAlCwdVpZwchLNadhCPsbR9aJoJuwJn6MIWxL7T6g0kRdRNQSNOs+3EXdAK3KZKbYZZjjfsLjDKdo1yTQNe+f4sIYUVbgjfkVLXQLmrmqbnk6tk6vguLxf3dhPbznWUnKZRjC/sWefhq6MxZD2D9Y238IVD7njZGr0Kx7ywo8tSv8G6OXYwjdjMfeFlP0Sor33oi7uKu/IGppNx9n7nAUQz7WhM/L9usNLkK7/R8AhTvvRXfElduqB0P4RtwlHdCdMQGf00cx5FUbzAQWrEsrVSJURQvZgqKV4Mof7F3VnIYhfAPm2KUA2Dq8G3DcjBHe67chdDthvR/EbW9D8a5JVefLkFd5n2UFzSqDGa0IQ8h2XAV9AysLtCLCejzu36fB5r3eKkEqNTTrbgxh/2DPGFe2DQ+G8A24SzpWeYwrMth2orvCcOUPAFRsnV71FtwsKRTvvifg/hrSdQa6M4KSA5eiWfeiWVIxRa/w33PKX2N8lW8+1V+LdfRy+1GNeQHXTHPszxC7mJKUK9BdEd6W5cSPceYP9nbfBtwl7b33nmod3nU0QNom778lOVUmUVRvS4pmScVd3MVf8Rja9flK6awd3go41tbET7zvKXZKUy/F2t772tLuS5y5wyjfoqYYs0HXsCZ+jGbbS+GO+wAVS8IX3nUSPsFV0A+PPd5fcD/0fbhRtFI0awoAxog13mApZFvZ/r37MYRtRjXmoRr/ovTARXi7Cpa1vKhOHBnjADeqJTUgb6E9HvO3woG3rGeKXkHJ/mtBtWOKXYox7F8cOcNw5g5D95grHR/Vsh/VlIUrf5A3L6H/YIhcgzP7BIyRa3BknYA5/jtc+QPQPeay4Np3bO3obiMh3Z9CUXRKDlyMMWItzuzjcRX2RbUcqPzF6ZR9BrVsG97fXsNWULdsR3UwY4pehha6FVdBX1RTuW4AFbtW6YE3VNV8AEvbb4HaFz4BrO3fQwvZTtGO/6G7Q+qdb28eAy/2imqvImHVdE99vn4XiqEQ3RVZbarAwm31F2NL/Hc484aA20pVhZfytfOV3otZiu4xoBrz0XUVR8bp1e6v4sVANZa7sasO8JixJs4FvDWYpamXVD7eQfrWBpxDeFv0TJGrcWSfWLnAozj9BYLyXQC1kK2o5rSyoEjBGLUCY/hGivdf56+9AoIWmn0tcMV7bsJd0qXS+76uVL4bFkBI1xcqpQsqSNnC3G4eSpA3zG1+wBz3I8W77yyrTfOg2fbgLm1X9hk8aCHb8ZQmVqpt86kqkAG8BbrYpVW30Ji9rS3muEUYI9ZjjFjvT+sryAZfsTQgWPEuO/Q9K2opern3Q7rM9C+3p59dbSBjilmC7jGXW1KXroG+4DDwt6FoBdg6vYYzb7A3eAvC1vENNEsa9vTTcWSdHGS73t+moazLlGrMJ6znNHSPCU9pWzTboZuhuU0NE4Joxf7gtiLf78kQtglXYW+UarqgWdp9BYCrsI+/9aQmlviF3v+UVXaY23xP8Z6JldKV7x5T8aTWzAdwlStUGKN+D2xhUxxYEj731uD7N1jD91ju3qEonlrX3do6zAGgsCQJ3RnrP9eq3k9goFp1wOnPTS1zUj5Pb6NZUnFkj8R+8Bz/ckNZ1xjNnF5pHKMWsgVj1GrcRT0wRS9HNXkL0rpuwpFxGoaItVjLCrCBv+dqgpmy3gSlWgnOnBGHCm5lrazlqYYCMBRg6/B2wHXeEv+dtwJNr3tLmWZNIaz3lOrTlP1uQrtPx+OIQTV5x+/4AhnvdpKr/L2A9/pqilxD0c578Dji65zPulKMdesmZQjZGXS5GuR7qMhXgXDo9XMBrzXLwUrfp63jqzjzB3srO8q9p9n2EdL1/wIrDoDy51Boz2kBXeY1634AzG2/xhT1R8Ba5QMZwF/WC+k6I2C5KeqPSuv6hHR+BYAiZxSeko5Ykz4AwBjmvc76Koh8Fa3lGcI3oprT/WUo3z3LELKrynueKXYphg5zsKefjl6Pc/pIcFQHM+ay2vlKJ5RqxxT7M678/ngcbah44VfKt2hE/Y5qzMaeflaldBX5CjuG8L8CWk8UrRBTm0U4c4bjKU2qsJY7oPDpX0ctwRC+vqyWzBRkbzqmuEUBLTE+mnUXhogNQbdbdd43YQjdjGpJQbMcpGj3bTV0yyhXe2nMQ9c18JirHCwc1uNxXMWdcGSdhO624SnpWOu8mdssCnjtyBwNujloWsWQX0MBpELQYswt+1/Fdaq+2SpaIZptD5aET1FUJ6ollZJ9NwakCe05FUfmKd4uUeW25asJ85Qm4i7u5r+QmmKWl9Uk1UwL3RE0mEE/nBq+yuuaItcETelr8TPFLMeefiaGiHVYE+YdqrktK8B4XDaKtj8SZAu1K+grhjxQnOjO2ApveNc3Rmyo1XbAewMJi1yHPf00HFmnVJGX4MfPGL2iyi5+WuhmFLW0UiCgGgNrRxWtEEviRzhzj8WVPyTgPWvSHFRTelmLSbmuEdErUE3ZmOMWVwpmjFG/oRpz0SxpZZ9vfUAwoxgzsXV6HUfWiUFbExXVERDI1IpedcuPf7uKjjHs38C8Rq4CVHRPha6B9aigqY657XwMoYcCTsVQFFihU+G64AtkfEwxy/1dT/wq/KYUYyam2KU4Mk8Ocl7WNH6jMtWYg7vidirxBbt12G4d0ivGTFRDob97rCn6d2/rZ/ZIb6VWuUPgrdU+dF/xBWW+Qpx//+ay87JszIiXL08Ktfk8mm1npV4IqmU/ilY5UA6osCoT0u0p3MWda9zP4fIFMjVRDDmollTchb0BxX99Dek6g5IDF+Mu6YAxfANqjYFq/ShaUQNtyfvd6bpSoVLz8Gi2fVVek9Sg41XKtQRV+G0rWhGqKb3KYKShqKbMOpVl4FDwFIxm245mrhws+n5HFctCR5OjNpixu6q+sSiKjjnuZ8xxP+NxhfjHOgRjafsNAK7CnriLg/e5r+zQgDUtZDum2CUYbHswRa6pFHmH9X4w6BY0awrWxE9xFXbHXdwVxXgoSNBCtmAM/6vKrmO2Tm/WMp+H+MYT+cRGLic97aoKqVyAwVv7We6GEtL5Je+7hT2rHeNisO3x/yjr0uJVkWpOR7McwJk7lIpda3ytF1VRDAUo6qGbhYqHEeo/bKztLCdaEbZOr/hrIOFQLVDAfhQP5rifvZMzBLk5GaN/x1DWj9eb3lv7qBizaxz4WfVNqQGCmbrcm8qOma/w56stNpQVZtUgg15tnV7ydx+oSWh37wxABVsfCei+Y8HONMObPFcai91WeXxS0Kyq3uuBuc2POPMHobtCUYx5Za2F/lTB163mhm2rYrKH8ucHeKdCN4TsxhCym4IKwYzvN2OKXoFq2Y8jcwweewKKoZCKtNAtKKq9UkHcm9FDrYGW+O9QDYVY4hfWq2tkQ/K1xFSkVnkee7B2eBvdY6E0+Zoq0lT+Tiq27la8Fpiif/d366rI2n5OFS1vgeeErcO7qKZsDCHbKdoxJTAftZ4pqVzBq6bCoFpKSOeZqKbcWm4b4shhrPo7C6hd74CKtebgDey00C2VurLVtoJMKfuMernjF9rjUZx5QwJafaoTrFXDVyNeG6oxHzViYzUpDnXbaQqh3Z8BoCT5Km8lZTnVtihXweHyYNIU+OMtaFuL7vCHMdlPoMrnbJvOz1Biqbp7W8PzoFRTeaBZUmvfK6EFsXV8p87r9FN2kazHkUvlrv5HkqM2mPlmQ5B+h0EEDWSC3GDK1wappoN4nNEVBiEf+mEZI//E44hFNeZiaTefqtSm2dcQuh1DaOBMTL7asMp5LKx21pa6GGVYyzdRXXEV9EF3RWAI34A1cS6lqedVWTipzWD9huC/oSnuOg9mrdj3tYOaxiemJ7lF68+K8m8EKZhU2excTYtIVTVDFWuvfTcac/wC7yDFaoTatnKFcT5znOeiB9yMD+VDMWaiO2s32QB4Cymh3abjcdeuz3/5PFc7m5Ra6s1XWRes2gYy5VkS5qG7DhXM3GE72UwhUSYLaQGXuNpFYuW7QAS0FpR9j12VFE5WN/BZnXMayNbxNezp43CXdAm4fvRR9tBP3c1n7pMp/535at0MoVsp3PpE5Q0qDmxVzO6nWQ4S1uvhcgPhDx2LkG7Tg9Zc103DjwEytfmBiOwBZBCJYshHd1uxtn8fZ/5Af2u62unloIVazZpCSLfqKy4qr7MfW5fnKU25DI89MeC9qroQKqoDzbq7bJya4q8h9h1PrVzLg6K4vR37DHkYwzfgyD026BgatVzlh7nNQtzFVV83jRFr6hTIAFyk/cp2Q82taMbo5dVeI3yVE5W799RG5WuiopViiv7dG8zUokZf9beaNw7VlNEk3bsqsrb/ELe9zWFv57tNBzg/Jhm+v79W6QO7DDfs77lpAxnvzKF1blluBMawTUFbUpqKopWywPwQJbqJ3vY5zZaPpnDUBjN2V/1qIYy48FTTDUYL2eINJkoSmJliZ777BBZ4RgR0MdAsadg6zMbjqHoWH2PkqiqaTuvP3ObHeqwV/KL2Y5gZS9jXeGIXU7T9IX+f+KoCmeZgsO1ieF4Y1dW91SRG8RZKeij7WcGhcS/GRm6erkgrG1RbdW31IQ5zLvO6reaa7SbWuXuzSS/rclauL21ot+dwFQafbacqijEPrU6FXu+5U74b5x7LFVyudOXvstdhPacBhzfTU6XAD/gyLHAszmLTvcxWh/N1HbcdrKZ5sdlbOPiM2s5+FZxm24ut05sUbH6atkoOvs4oxyY+R7jbQ1jkD7hLKnY7Ldenu0Lf6NqMm/MNhC/v8AMZCOs9Bc0eVeNEuHWhKB6mxdzPcrqzsM2hwpAh9ND0pdWNOVCNwbu0VkczZ2BNep+iHZNrld7a/n0Uze6fUam8Y5UtpIasw390FRcKHqxJs9EsaWghuyjZfx1oRZhjfsGZd4y3xa38mBNLmr+rYEWWdp+VDV6uDY+3tdwdgoqH1VZLtalVS8qhcUg1MMX+Uss8HKKjcKyyhX0UETwUagHPUKlHt8CGUvOYp5qVOj2QXXlMRtUOlWtUc1qdx9CU19xTAreEQAZ8lSC1nBSmEVnLPZZCwVPWItpAE0u0EEdtMKPU83uMJp+sIBfaCArJQscYWTajifUAY7R9jNHWs6A0+OxiVQUriimj5QQFiqfah4+phkI+NT3GDQ29X7W48mDsOjKE/83G8L9rTlgNvcK/PnV50KLWADdmxVBMp+ivyLJVfnZJVW43z6WN281ljv/xhymi0pTJdZ7as4EYDQVAxYH2tZ/pqT52heYymh/4mriaE1chpOsLeJwRzC4OI9kQeOn0P5+gvttW8/3BTPlALFgXRZ8IivG152i4MdC8TxF3mxu+9nVym1igaWt1VWMeod0fr1Va37TUxqjfKwUzn5sfY6oWzZd4v89nTa/SR3dyicU36YK3xcUS/y3GiA2YYlaUda+t3fWiphkoVVx4ym7xloTPMEZswJ55Et+GVV+9Y+3wNlqQZyQFMzjhOXYY6l5wtehOPjc/xsiw9hxOV65GfWBrg3W7ajq9lH0cq27hI/dY7wK9Dp+hXGtYdeM2aqJZ0irNDCdaBg0335n+R7YexhXOh5o7Ow3qqA1m9Lr8yMtxQ9Aam5Ehi/ml7S+oQfqxA7VqNvdRtSq20Qys7T+stkAF8FV8BtSy/3Vtaba9ZdPJNi+PEvgvwOWmr/lBLax10TGUEg637ls1FJAVv6pO6/hqeNtFLCGkbd27bx2ubkoKwR6fud5SOUg1WPejGBuv0Dopvv5BjI+ilaBpJbxgqdz1Jtizg2priLKN7SF1LxCOUDfjqxP/b8z9eDQ7r9SiX7Qh7K86XY+ORnV5iGF1yh/lrmoyvVUHlGvRC+n+ZMAU+e8Yn+Vvo4eGeIb4ydo6fnF7AyzfZBjm2GU1hoaGkNo/mHZHRP0eBmpTvEFggVY5kBmn/oFJKWZpLbZT0xjIw+ENBluXRWZvi2IYJQzf+BlEBZscqCqt7dOKujDjoKtygF6qtzynOj14jqDWmaM3mKnnDze0ywzs5srF2KVRRZXqlx6Ojea0omIohdZ6oagpkAFYENqwgQxQNif/wAbfbl35vrUi5dC3u6DryjptI1/TMMU1/SwjvgtVZsQeoHYPkWxIyRH7sVkCp7NMrqKvvrVDHZ9mfQT50jyN/nXssmbt8AYqBYC3NeulNiagdgWXyg9cE42h4l3CEaQ7gFqhVWOMtp7vImPxfa+HY7C6hbPUtTznvISWUz3mpQB5avCC1KummdgVheFU7mLZlNpHLyRNDT51fEui4sFTofTxgPFTSMH7V5Ghih4PrbAlStTeVsuEgNffmB6in7qH9/P+D6jdFPgt2VEczNR2ZplAWUECmap8FRbKV2GhPFLwPjFaKrVp1LN1frHOYxmORIrqrHLq36ako1CgKOwyHd5Pxfewu6Y0JyKM9i4XTrV5blIeRan0rIAzkhKrSC3qwhCym58boMArGoZmSeNX012cyaHZ7x6Ki8FZLoBxKcGeylTZQa3mwfm1cY3xRyI8HsapfzKCwx9Q3pDyw/ZxQljwYGVQ5w4MKa36OVNNJStiXzNUAdXNC8aXOEHdzBuus+mj1rILsur9VGvS1qDZduIu6YgxovnvtaJp9VP3AGDQm7d7ckM5aoMZt16/YKY+/mNYRLaqAu1rTKtZDqAFe8KraBabzSZGdmreGsL6+jCi6ieTCyEaVpKaQfkuZAsrtFinaxoXJFZ+7ld598bF8Jcl+DOy6spXRAlVmj8wqKt1QbqiisrO0VZiAB42fljrdXSPE6fbwXU/XIetbo9AEUegjkWHM0VSy1GvkXevvPIKnTp1wmKxMHz4cP74o2lndmoInnq2zNTHA3ExAWMuhBDCZ1Kbmh6KKI4ED8fFsMNUfVfAHxuwy65LUXg3IoxzEts12DZFy1LXNvftRiMnJ8Qw5++jt1uvCORRjow2jToHM59++imTJk1i6tSprFu3joEDBzJu3DjS0xvnqbSNxaM33bSL34eGsNvY0hushRDN4acQ6S4mGp5LgRnRUew2yb3nSOWu47SsT8RGka1pvLTh5UbKkWht3ErDdGttbnUOZl544QVuvPFGrrvuOvr06cPrr7+OzWbj3XffbYz8NZqmbJkB+E+7pn/4lhBCiKOTvb7PHxCtRl1bZpxH0OxVomF4jpDRJnUKZhwOB2vXrmXs2LGHNqCqjB07lpUr6zbDU3NrypYZIYQQR7aJDTD9d0O6s4XlRzQ8dx1jE5cEuEeVMHfN4a7naGyZyczMxO12Ex8f2MoQHx9PWlrlpxTb7Xby8/MD/loK9xEyg4MQQojmt8xmrTlRE9onXZuPeHotW1oyNJVLE+LZbK7Lc2dEa6fWYv7EjVGnNUFOGl/9H71bC9OnTyciIsL/l5TUcmaFqu9zZoQQQgghmltt+5dc1y6ef80NM0ueaD1qMxV8runIGAJRp2AmNjYWTdM4eDDw2REHDx6kbdvKU05OmTKFvLw8/9/+/TU/gLGpSMuMEEIIIVqrYA9iDWavtNIdlfI1jeEd27Ooiklm3LqCfjR2MzOZTBxzzDEsXrzYv8zj8bB48WJGjBhRKb3ZbCY8PDzgr6XQa12nIYQQQgjRsiwOaVldG0XLU6yqaHrwNhpNqU3bTetQ52kMJk2axLXXXsvQoUMZNmwYM2fOpKioiOuuu64x8tdoXLq9ubMghBBCCFEvIZ4jpzAqGo+1imDmSFLnYObSSy8lIyODRx55hLS0NAYNGsSiRYsqTQrQ0u0u/Ku5syCEEEIIUS+rrBbOKyyqNk2m2qhDo0UrYDsKgt56neUTJ05k79692O12Vq9ezfDhwxs6X41udPyVOPP7NXc2hBBCHGG6OhzNnQVxFCisRaAyO7LldO8XzcNYRcvM355OTZuRRnTUhuwmzUxpylWNvp92rqZ9OGdTm5ecitVzaGa4E4pLuCc7pxlzdHg27d4X8PrS/IJmygm0cbn4OvlAndapqm9sS7Jmzz46O1rvBBxn11AT2hCuysvnmrzgU9mv3b2P7ul9atzGkNJSOjprd5yHlpTWKX9NZXxB5WN9YUEhp9XiO4h2N924yBkHMwJef5WSxo25eU22f3F02mKqeWC/PChTVDVFc6KS2cQ5aTxHbTDj4y5t5/9/adp4SlIuR/eYeP9AGrfl5DKusIj/5ObxSlo6oR4Px5WU8MP+FEZmBu9WF1kcDUCU282Dmdkc2H8LpsJO2AoT0Ys64cg63vv+weNx772W4j23YMzvzumFRXydfIDbcnJrnffyN9Dji0sC3htbVFzr7YC38NTvwGCuLitAnVpUzG97kzmmQiGnrcvFYxlZ3JaTy2X5BfRwOvk6OZUuDif/l57JawczODYnhqJt/8OeOdq/Xv/S4GOUetmrrsEcUlrKl8mpGMoK6G3KAsPqCpMdnU5ePJjhz3f/UjsTc3KZn5zKxt37WLQ/hc4OJ9fkFpKQcjKnFhUzLyWVMwqLmJuSCsD8ZO+/92Tn8FBWDieWO7aarjOspJTJWdlEuypP792/1E7R7on+1xNy81m3ex/X5+bxTPqhC0ei81CQO+tghv8zljc1M5uU0u6VlvezHzqWk7OyeSIji79272PF3v3MS0njvILCgPQX5ge+Ltp9B+7SwNkHLR4Pp1Q4Z57IyAKgvdNZqbBWldqcv2Ydril75lT541CTj1PS/N/Fdbn5DCy183JaOhcWFNKtrCb83qxDgXRPuyPgWPkckzKE0wuLOLfCcSqvqoLwir37Awqp/1f2nY4qCDwXfNseUVJCXDUVGvPKzrWKTiku4c4qjqURSM46i7CMYbRJPZ7wLXczfPt4/zHoktuOCLebt1PTWZCcSsLOS1FL2vJAVuVKhuElpfSx23ntYEal8+bmnLyg349R1zHoOjaPhxCPh0vzC7ih3DGZm5LGhQWFPJyZDeA/7x/MzGZYSSk35RxKaymrCOlnt9Pb7uDissqD0UXFfJmcylOZWdyYmxew/Qeycng2I8u/ru/a19Hp5MKCQm5LCWPT7n28sUelR2Y3Qjw1T8M/qsL1syJf8BTsdwqQVO44nVWW9o6c2gczcUXRxBcE3lNuysnDqOs8lpHFTTl5uLbdT4g9lBOLS1hfodLF4vFwRmGR/yF5db3+VzTzYAbvph7k6+QDXJZfUKvfadsGqLh7M/Ug15f7ru/OzuHZ9MoFrgm5+YwsLuGMsmPdJUjlyPiCIo4rCfxeTyu7z1YU6XYHPGBQ0/WA8ybYteLG3DyOLy6ht91BotPlvz9VpeIDDH2/t2ll19mK1HLnWnunk7MLi5iUnUOH/EMPQz1oMGCvIVY53HnMKp5LJo/O6ArLwitcL30tk77fS+9y9/nrcvOZWO7aNjUzi+4OB7NTD3Jndi610aOackMw5c/fRzK9x7uq78uo63QKcj4lOl3MS071l5EA+pa7vyzfm8xVFSqg2rlcTM7KpofdEfDbjHa7aetyEeuqfJ8Jc3uYkFv1Mxkr/hbjyz6H7/5d8f1jSkrpU0XlYZRSSO92R0bLnaLrTVeVm5+fT0REBHl5ec0+s9ncP/Yx+ctNKMZMLPHfYc86GU9Jx3IpdKIpwIGBqYb3udiw3P/OTNcFzHRdSF/bcooTF/Df7BxeKryJze5ueFyRhKo5fGWYzhLPIJ50BWn9Ue3gCZzzfY/lCgA+co1hmxrB+64zGNV2JusjCvgyOZWVWiIvFEyktM0KnHmDMRe351/Lf5gTHsYc+0VMKt7COdpKvgwNIcbtpl1RDOM8DxPa/SkiS6KYmH+APRYnZxUW08/h4HjH80R2epHuzmJ+2f8U+YQA8LXpIWJNe2njcvsvgj/brNwTH0eU283yfSn+PPcofY9tlmsDPscNjnv52TMEymqDLjJ+zwUhn5NaNJjJIcNxlyZwa/RTjC8sItrt4Q/HYO7ULsYQ9i99Ci30iJ3HdXn57DEaObm4BKuuc7XzXq7TvmeU+i+Gsi3369gFRfVeKGLcHoaVlLLRYub44hJMgAfYYDYz0G5HA7Z5EvnRM5SJhq8BuNIxhRWe/v7j7rPL05ZrnFOYYXyFRe5hjFXX8Z77NH5Qe6EY8tjJf6EsD885L+YV9SQU3IzxbCcmdANrCsaxw9MVa8fXCDfv4auUA7xfejH79TheNr3EohAbU+Ji+OhAGr0cTooVhdNLZ5Ksx3JB9IvYbNu5qKCQR11X8VfRaNxoKFohXQw7+FZ9lr/MJkKK49mpxDLVeS02j8rHpiforB6aLn1g6Zt8G3obd8XHcntOHmOLS3AAGyxm9hUN4T7nHaA4Cen6HP1y22DOHspodQNb9PYs1Tpw0NGNn0wPEK3ks1e10psMrLrOnPAwno+JopPDyVtp6ewzGuhnd5CpaWRrKpvt/bmctWw2GdlgNvNUrDewfzIji0i3m5nRkXRwuhhyYDAfuMcwKOwHHnQtw+bxcL/jZn7uvBLVlOs9bzLM2Ao78Yr9KlRzOopqx13cmYcN73ON4Ycqb9BPOS8nOmoJiuLm5gJvAHaT8WxKYv/klOJilLy+TCu9k/9o3/Ow8QP+NpnAHkeOLYswj4fdRiMJLhfTC+7lkdDneaBNDOEeD6+lZXBe8bMYUcjTQyiO2MKT2gecX5RNf/s72DGhmg+ge0womp2d3O+vD+3meglCdnNycQGJ8V9wYUEhm8wmot0ezigq5l+TkZ9DbNyQm88Fie3wKPDmXidPu65kkT6IuNA1dFYPsDluK3F7LiLarbNG71Xhk+tEUkguoYDCfYZP/ed6p9KPA64xL7jOJy0kk8/ybmSP5RoAnnFexmvuszGE/Y27pCPfq0/RS93PPyYTt7aN47q8fH622TitqJjjS0p533E28+2nM8v4Cqdof+ECfrVZ6V9qJzZI8LDfE0c7JYvfPP1Z6BlGgamAG0zz6eh0ssZq4ZSiYnyP89P9Vw+v9Z5u7PAk0Mu2ChWdvg4nyXosn7lOZpJxHk+6LiY9NJNleeeTq8dW2vceyxVsMJtYZzFzcX4hharKLNd5dA9bhqOgH8MtK+leqnOM/S3WWydwXbt4HBjYoYXyUG4yZxQVEebRUYFHnNeyXEvi9NhXuDo/n+9DQogpiaCguBfPdd2Brrr5c89+LGW31Zdc43nedQkdwn7D0fYn+qcO4FfXsdidsYT2eBLwBo1Lsq9nrXMgppglRMb8zLTMbAYU2rjTeTNvG2fxlOtK5rlPCvgeX+ME5sbnE5/Tg48c85jnGs1ZpqXkqRqqM4K7zWO40r2OgpCDfBIeyqtpGXRyuXACN6mXs9RxIqCgGPLwlCYxWNlCbPx8RpQU8x/7TgBecZ3D7YZvKpxp4ASuToinl8PJhLx8NpjNnFlUxA6jka5OFx7gFPMErvf8Tn91Dze2i+f63DxSc0Yz1vYDK2xWJmflYNR1NGCF1UKRonBacQkZmsopHdqjucyw/T5O09YwJuJDJreJ5eTiEpak38YGHvPn5yv3SM7Tfufr0BBmRkVya643CDy/LNDRgUJFIUTX+cQ1hisNi8nQVLI1jQcjO3CcK4u7s3PRgDxVZafRSHxpKO2VLH6wWQnRdU4oKeVvk4l2Lhf/FxNFlNvD5LIeCDl6KJFKIQWqwhKbjZOLi/kxxMYxpXaWW62MKCklweXCous4FAWTrnOJ/VHW696KqueNr3KWYQV2BYpUlbPbJzA5K4fTy867LE0lyu3BN4Fuj9L3WGObwPEdvc/t67tvFFuKTuR3853YFG/heoF7OIXD7uayPlZeyFnP7H9mV/pd1MZve/cT4dG5Ny6GLE3j7bR0NMAFpBgMvOY6l6TIn7mkoJBQjweLrrPZZKJzWYuwSdf9A7OX2KzsMhr4T563wuKeNrHYFYVXD2YwqPRNlpvvJkIpJl9VGJOUiJYzmN/yv6VEUcjTVP42m2njcpPochHvdpOpqrgUhQnt4rm4oIBeDieZmsYBg8arUZFMys7B4tGx6DrHlpaS6HJjVxTmO09msHk1s0qu55TIjzjGmU+k28OL9ivorBzgbX0cE7X59LGsY7vJyNS4GCLcbn4rV/aZHxrCq1ERvJyWQZzbTamqkOByU6Qo3NsmlhU2K88fzOC04hLS9CjaKoEVSYWKggqsdfVmqGEzL0VF8EFEOBNy87m3LNArURR+DrHyv7hYrsvN5/SiIjQdEhwqZsVO+cefTnbewBPGt1lrMdPf7sCm6/xlNqHp3uslwK/ufozS/q70Hf9y2TZO6dW8Y94bIjY4aoOZa979g+XbalfbrOLhVHUNx6pb+c59HOv1bvhutxeqy9mlt/NfmOrrTu1Leqj7ucN5B3pZg5kZByPVf1jp6UMpgcGPCac/kBhe+jIHiQ4omN/hmMi3npGgloLHCGiEUszflhsAGFz6OrmEYMJN+Z/FEGUb47Q/uVJbTKjibd3Qgd+tFro7nLQpq4FZ7+nG+Y7HuEBdzg2G73GgcYvjHtKIqfDJdLoqB9ijt8VddjluQw5RSgEOjOzX43CVm4ei/GeY4LifFZ7+ODEQRjGbyvL+kus8nnddzD/Wqwip4fS9xP4wRsXFP55O5BJGG3Loqh5gpacvAL+Z76S9kkmyHssJ9llQQ5P8QGUHx6v/sF7vxkpPn2rSu7CpBXTRc/hb7wwohFDCHYaveMN1FuO1lTxg+JSrHVP8504U+bxleoF57hOZ6z6l0hZ/NN1PDzWFXqWzK50P35v+S291P9+7j+VW5z0kksHlhl9I1WNY7B7MtYYfOVbdytWOyZRgqfYzBtK5WVvA3YYvsCoOnMB810lcYljmT5GmR3GK/Xm6KSl8Y34Y8D7M7YXoSAaV2jm1rNbbV0gdb3+CTXoXAAy4/N+/aknG2v497Oln4MofUmWO5pseYbC6I+h73oK7TgRFbLTcRKYezlD76/7z6gPXWB52/QcjLp4xvkknJY2JjjuJUgr4zvwgAP96OnKW40l2Ww5VRPQrfZtCAufqN+LCgCvo8Ryn/sHzxtc50T6TbA5d685SV/GK6UV+cg/hIed/6KoeIIRSbjAsZLi6xZ/uIvsjQQKW2gujmKnG9/nGPYLlnoFVpnvL+DzD1M2caJ9JHqH+5T2U/fxo9gbuOvCo8xqmGd/3v+8LkCDwN5uv2whXAmttH3FeyyfuMah4Aq41PZT9dFNSuNbwI8PVLez0tGO1pzcfusfSRsnheu17PnGfwkLPcQAMVbYwz+wtxD7o/A8fuccQTUHA8Q3Gl79kPZZQSjjP8Rh79Hb4zshOSioZeiRFWFHw0EfZyz49ngJsPGaYTUflIHc5b6ercoB1end0VE5V1/CW6QV+dg/mBuf9ZXvSOVb5lzO1NVxn+AGAyxwPscoTvEtgFyWZn80PoALj7E+zVe8AwEnqOiYbPuV+5838rXehYnj3inEmHZR0LnA8hrPCHD6j1fVMNnzCvc5bytb13r/u0OZzrLqFE7R/Kn1/Fc0xPsPJ2kbAWzGy0XKT/73bHXeyTW/PSPUfHjW+B8Cg0jfIJZQXjK9xgfYbv7v7cLvzTnIIR8XDLOPLaGoRLzouZ6i6nSeMgQXr0fbn6avsobOSSiYRTDe+w2q9M5faH4Oye8Yjhve52rAII9C/9G3/vcB3n6tYKfWVeySvuM7jJ/MDAcuPLX2FPy23A97xAmc7nuI70xT6qnv9abZ62nO98356Kft42/R8lcfJZ4z9WQp1K2O09TxlfKfKdAf0aBa4R5Cjh/Ga+xz/ciulnKhuIkHJZKrxg6DrznBeyHLPAJL1WDKIYo/lCu6Ni+HH0BB0l43C7Y+g4OEGbSF/eHqxUe9GbKiZNQ+NZda6Wby96e1qP0O4202+Vvl5I75u1/c6buF50+sB740sfZEMIvndfAdxSu1bIa9yTOFD03T/61scd7PIM8z/eoiyjTxC2Kkn0l5J5zfz3TVu060rdLV/SARFjFbXMdXyJlEVKlXudNzON57jg6xdsfrE6zfznSQqmXwfYqN3qYvO7sqtQcvd/TlR28Rfns684jqPmcZXsCoOPBzq9tSp9GOGKNvIIpz+ym4GqLvI1UN4zX0OOip7LFfgAraaTPRyONCALZ4kdutt+dUzIOCcusoxhfWeboRQSgflIGO19bzguggXGldoi7FRyv+Mn7De042f3MfwgPFTsvQwznU8QbIexwfGpwICmg9cY2l/1WuM7tWmxmPcmCSYOQxvLNvJ9O+31JywBbtT+xKT4uQ516UArDffRJRSyP85L+FV93lB17nH8DkAM1wXV7vtc9TfedH0ctD3vnCP4j7nzf6gqyGVvymVv+EqeFhgehATLsY5nsGDSm9lL5MNnxCilHKTYxJ/mm9FU3TsuoErHf8jVY8hhbhgu/HrpiRzkfYrr7rO8bdONRUVD546HEMFDxqegODPpw05jNd+53P3SeSXK5Q2FDMOtlomAHCe/TFS9WjaKLn+oMQnjlxOUDcxw/Ra0O185jqJB1w3H1ZeQiihp7KfZ41v0FUN7KZV/pyJpIASzNgxscx0Nx3VdC62P8KfVQQJ9xjmEUUBj7gmAAoj1H84Tt3MLNcFdfqeahJJAXmEVPr9vGB8lQu037jc8aA/2G5sCh5MuAis5/PRuUD9lU16F7br7eml7KO7kswfnl4cJNqf6mR1PX2VvbziPpeT1Q3MMT3rf+8fT0fOckwPsu1DosjnGu0nPnOfTGqlypBAjxlmM0jdwXmOx2v9nfiuKefbH2WD3rXBrlttySKdqKD5iCafTkoa6/QeVa4fQaE/UBhZ+iIHqNyqVLXgBbDqdFEO8Iv5Pv71dOTMar6TBwxzua2sNaZT6ce8b5zOidom/2vwnjdvGmewX4/jMZe3dU/DTScljZ16QpV5s2BntvFZ/tB7McN1ESacOALaWXV6KvvZqScEXOeiyOcN0wy+cJ/Ip+6Tedn4Eiac3Oy8Bx2VF40vcYK6iUsdj9BX2cNCz3AcGJmozec+o/ee97brDN5wjedPy20ADCl9nWzCiSGPtZZbAXjWeQmvlN07VTxMMXxMERbuNnxJnm5jnvskxml/ssHTlbddZxGqlPCbp78/nwoe+pUFZi+aXvEvd+gaPezBAxUfAy6mG97Gojj4w9OLND2avzxdAn5rPserm3AlzuPfUG9LTMHm6ZWO+dCOUcy7dSQz1s7g3b/fBaBtSFt+uugn+r/XPyDtMSWlrLVWrpTZtHsfQ0tfI5MI/++o4vXpbeOzjNXWB6zXufRD7jd85j+PPnKN4SHXdf5rTQ9lPw8aPuIF10Vs1LtVe1wu037haePbHNQjydAj2eDpylUG77MO57lPpEQ38aF7rL8yAA795pe4B3Kr8266KylsKqtUrK12ZHGWtopP3aNxYOAc7XeWuQdyovYXzxnfALzBfnclmTV6T/+2VTz0VfYQrRSw0dOFXMKq3c8i03/ppe7nN3dfTtD+4U3XWTzluhLwnk93aF9xgfYre/V4rnVOrjHfPZV97NPbBK1kC6eI+wyfEaPk8aH7VFZ7erP6wdOICzMH2VLTkWDmMDjdHh7+6m/+PpDH3ymH+id2jg1hd2YRCREWerQNY9m2DGo6QomRVs4fnMjLS7y1xeEWA6FmAwfySnn47D6s25fD6l3ZTBjZked+3BZ0GzeO6sxbv+72vx7aMYo1ew81TT5xXj/25xSj6/Dm8l0AnDMwgTP7t2PW4u1sTs2nLVn0UfdyxvnXMrp3PDe8t4YN+3MD9jNhZCfaR1l54rvN/mVd4kLYlVF5HEpnJRUjLn8NLcAP7qHc7JxU5bGYckYv3LrO/y3aSlK0lf3ZJXSNC2FnRhE2k0a/xAj+2J1d5fqfJX3BsIwveNlzAc85Lgp4T8PN6f3a8t3f3ha1cIuB/NJD/UOPU/9lqukjptivw9luCP8c8H6v94/ryb8H8tFUBZNBZd7aZP86iZFWUnK9LQcJERYO5B0aI6QqYDVqFDncRFiN5JVUP5j61D7x/PTvwaDvDe8czX3jevLx6n3MX5/CjEsH8uv2TL5clxI0fTBtwy2k5dc8UHtU91h+3V65n/nj5/Xj4a8qNzMDTB3fh0e//Tfoeyf3jGPZ1oPstlyFR1cYZH+DfEI5q387lm5NJ8RsIL2g/NgUnWu0H8nWw5lmfI8f3cdwrvY7IYqdAaVvMu2S4zl/cCLTvvmHnzen8+qVQzj3lRUAPHfxQB75+m/O6t+OQR0ieXB+8PwCXKot4RnjW6TpUVix87DzOr7xHE//xAg2peRxzYiOXDOiE2NfWEYoxfS15bK6OKHG49enXTh7sooodlTuz9wzPoytBw9NCmE1apQ46zbQvOJ5dohOKCX+FqBebcPYklZ5AorubULZl12MPciYrbevGcr7q/ayfFsG4wcm8O3G4BNIDGgfwV/JlWtTe8SHsu3gofEBFx/Tns/L/V4qqnjdAuiuJJOsxzJE3c5fnq4UlGvR6tYmlB3p3u3Hh5vp1TacZdW0kPvy89T5/fnf/E1VpvMJ9n30V3bRUTnIAk/lBzsDmAwqDpeH+07rwfJtmfyxx3t9CjUbKLQfur4M7RiFpiqsrub6VZNOMTb2ZPlarnReMb2MRS/leud9XD6sI5/8sS/oepE2I7nFla8/lwxtz2drvN/P5cM6BKw/bXwfppX9pm86sQtvLt9FHDnkElapRac8G6XcqH3H955hbNOTGKTs4CvzI8xxncY01wRCzQbaR1mDnpuHo12EhdSy34XNpHHziV3JKXYw5/c9tVhbx4A7SEWPzuioTNbmWMknBBMuf4+GrqUf+HsLhFPEUHUryz0D/NsIsxgoKLu/WCnFg+oP+nvEh1LscNOrbTg/b658zVfwcJ/hM9Z4epKqx5CuR1ZqQRzbuw0/b04Hqv6tA/RuF87m1MpjKMzt5mGKXANAScqluPIHB7z/+Ll9uXpEJ6avns7HW7xB6Pqr12NQDZWCmd52B5vN3s/WyeFkj8nIkxlZfJ19C4s9xwDeXgndlAN84TkxYN2+ym6+Mz/Ie65TWe4ZQJYewYayAGWN+RZilXz6lr5DEd4HfM68dBCKAnfN3RD089bEgp1phvf4xTOYHz3HYlAVXBWmHp5s+JhrtR85x/EE2/X29dpPVXxB50pPH770nMg5AxP4ptx19sz+bckvcfHbjtoNrrdRSpKSjjO6F5aiZP4tjaI2QVf5sktd3HJSV15f5u1GetEx7Xnu4qpb7puKBDOiaRzYAGFtwRwORisojTg7itsJqRuh3SDQ6vwYJNGY8pLBUQRxPWu/jscNqgaOYnCVgq1yLWO9uV1wYD0kDgFFbdzzUoijmaMITE3bct1oCjO814uQ6lsBW7p9+fs4a/5Z/tebrvUG+xe+/xLrd9qYdvoYrhnRiam/T+XL7V9y5+A7uXHAjQCVgpmnMjJ5MiaaIlXl1bR0RpVNoNOp9GNeuWIIZw1oR7Xshd7zo+I12GX3/lmaobzncoAhWIuzaGkaIjaQ0qKoWcKgptuXZoT2Q5tuf6L2IupRw6WW9cM22bx/DUkzQNKxDbtNIURlR0ogAxBafdfj1iIpLKnSsk0Zm9imv0lIF9D1MQCUuLy191aD1Z/u9bGv89ya59iR6+1NsseTwPf7d7HHaGBQHWcKA8BcRddmg9n71xwkkDmqHPVTMwshhBBCtCaKonB5r8v9rzekb2Dqyqn+11+kTGPi4on8mfYnABbDoTEUxycez/xz5/tfzyi6hdHFr/FswX3+Dk6/uAc1av6FaEjSMiOEEEII0crcN/Q+PtnyCQBXf391wHu7i9eyu9zEgm1DAp8tBlC0+3ZUYy4eewK5wO+efhxb+irnaL8zz31ipfRCtFQSzAghhBBCtDImzcSQNkNYl76u0nsqBqaNfASAGGsMIxNGVkrjKU3CUxrYXS2DSN5xn+l/LUMRRWsgwYwQQgghRCv09KinOe2L0yotP6ft/zi/+/nNkCMhmp6MmRFCCCGEaIXahQafaSzJNqhpMyJEM5KWGSGEEEKIVuqXi38hrSiNA0UH+N+yJ8naejdqF61Bti29zERrIMGMEEIIIUQrFWeLI84WR/+4/ny/qg1feYI/LFeII5V0MxNCCCGEOAIoZSP2m+5x6EI0PwlmhBBCCCGOAL5uYToNE83IbGaiNZBgRgghhBDiSFAWfEjLjDiaSDAjhBBCCHEEUMqiGYllxNFEghkhhBBCiCOA0uAtM9LPTLR8MpuZEEIIIcQRYHt6IQDPLNpCdpH9sLfncHsOextCNDYJZoQQQgghjgAb9+f6///Wr7sPe3sHcksOextCNDYJZoQQQgghjgAf3TCcK99ezck94+jZNqzatAs2ppJSQ7AyYWSnBsydEI1D0fWmm/MiPz+fiIgI8vLyCA8Pb6rdCiGEEEIIIVqYhogNZAIAIYQQQgghRKskwYwQQgghhBCiVZJgRgghhBBCCNEqSTAjhBBCCCGEaJWadDYz31wD+fn5TblbIYQQQgghRAvjiwkOZz6yJg1mCgoKAEhKSmrK3QohhBBCCCFaqIKCAiIiIuq1bpNOzezxeDhw4ABhYWEoitJUuxXNID8/n6SkJPbv3y/TcItmIeegaE5y/onmJuegaG61OQd1XaegoICEhARUtX6jX5q0ZUZVVdq3b9+UuxTNLDw8XC6iolnJOSiak5x/ornJOSiaW03nYH1bZHxkAgAhhBBCCCFEqyTBjBBCCCGEEKJVkmBGNAqz2czUqVMxm83NnRVxlJJzUDQnOf9Ec5NzUDS3pjoHm3QCACGEEEIIIYRoKNIyI4QQQgghhGiVJJgRQgghhBBCtEoSzAghhBBCCCFaJQlmhBBCCCGEEK2SBDOiRsuXL2f8+PEkJCSgKApfffVVtem//PJLTj31VOLi4ggPD2fEiBH88MMPAWmmTZuGoigBf7169WrETyFas7qeg7/99hvHH388MTExWK1WevXqxYwZMyqle+WVV+jUqRMWi4Xhw4fzxx9/NNInEK1dY5yDch0UtVXX86+8FStWYDAYGDRoUKX35BooaqsxzsGGugZKMCNqVFRUxMCBA3nllVdqlX758uWceuqpLFy4kLVr1zJ69GjGjx/P+vXrA9L17duX1NRU/99vv/3WGNkXR4C6noMhISFMnDiR5cuXs3nzZh566CEeeugh3nzzTX+aTz/9lEmTJjF16lTWrVvHwIEDGTduHOnp6Y31MUQr1hjnIMh1UNROXc8/n9zcXK655hrGjBlT6T25Boq6aIxzEBrmGihTM4s6URSF+fPnc95559Vpvb59+3LppZfyyCOPAN5o/KuvvmLDhg0Nn0lxRKvvOXjBBRcQEhLCBx98AMDw4cM59thjefnllwHweDwkJSVxxx13MHny5IbOtjiCNNQ5KNdBUR91Of8uu+wyunfvjqZplc41uQaK+mqoc7ChroHSMiMancfjoaCggOjo6IDl27dvJyEhgS5dunDllVeyb9++ZsqhONKtX7+e33//nZNOOgkAh8PB2rVrGTt2rD+NqqqMHTuWlStXNlc2xRGs4jnoI9dB0Vhmz57Nrl27mDp1aqX35BoomkJ156BPQ1wDJZgRje65556jsLCQSy65xL9s+PDhzJkzh0WLFvHaa6+xe/duRo0aRUFBQTPmVBxp2rdvj9lsZujQodx+++3ccMMNAGRmZuJ2u4mPjw9IHx8fT1paWnNkVRyhqjoHQa6DovFs376dyZMn8+GHH2IwGCq9L9dA0dhqOgeh4a6BwbcuRAP5+OOPefTRR/n6669p06aNf/kZZ5zh//+AAQMYPnw4HTt25LPPPuP6669vjqyKI9Cvv/5KYWEhq1atYvLkyXTr1o3LL7+8ubMljiLVnYNyHRSNwe12c8UVV/Doo4/So0eP5s6OOArV9hxsqGugBDOi0cydO5cbbriBzz//PKApO5jIyEh69OjBjh07mih34mjQuXNnAPr378/BgweZNm0al19+ObGxsWiaxsGDBwPSHzx4kLZt2zZHVsURqqpzMBi5DoqGUFBQwJo1a1i/fj0TJ04EvN29dV3HYDDw448/csIJJ8g1UDSa2pyDp5xySqX16nsNlG5molF88sknXHfddXzyySecddZZNaYvLCxk586dtGvXrglyJ45GHo8Hu90OgMlk4phjjmHx4sUB7y9evJgRI0Y0VxbFEa78ORiMXAdFQwgPD2fTpk1s2LDB/3fLLbfQs2dPNmzYwPDhw+UaKBpVbc7BYOp7DZSWGVGjwsLCgCh59+7dbNiwgejoaDp06MCUKVNISUnh/fffB7xdy6699lpmzZrF8OHD/f1vrVYrERERANx3332MHz+ejh07cuDAAaZOnYqmadIFSARV13PwlVdeoUOHDv756pcvX85zzz3HnXfe6d/GpEmTuPbaaxk6dCjDhg1j5syZFBUVcd111zXthxOtQmOcg3IdFLVVl/NPVVX69esXsH6bNm2wWCwBy+UaKOqiMc7BBrsG6kLUYMmSJTpQ6e/aa6/VdV3Xr732Wv2kk07ypz/ppJOqTa/run7ppZfq7dq1000mk56YmKhfeuml+o4dO5r2g4lWo67n4Isvvqj37dtXt9lsenh4uD548GD91Vdf1d1ud8B2X3rpJb1Dhw66yWTShw0bpq9ataoJP5VoTRrjHJTroKitup5/FU2dOlUfOHBgpeVyDRS11RjnYENdA+U5M0IIIYQQQohWScbMCCGEEEIIIVolCWaEEEIIIYQQrZIEM0IIIYQQQohWSYIZIYQQQgghRKskwYwQQgghhBCiVZJgRgghhBBCCNEqSTAjhBBCCCGEaJUkmBFCiKPU0qVLURSF3Nzc5s6KEEKIFmr58uWMHz+ehIQEFEXhq6++qvM2PvvsMwYNGoTNZqNjx448++yzDZY/CWaEEOIocfLJJ3P33Xf7X48cOZLU1FQiIiKaLU8SUAkhRMtWVFTEwIEDeeWVV+q1/vfff8+VV17JLbfcwt9//82rr77KjBkzePnllxskfxLMCCHEUcpkMtG2bVsURWnurAghhGihzjjjDJ544gnOP//8oO/b7Xbuu+8+EhMTCQkJYfjw4SxdutT//gcffMB5553HLbfcQpcuXTjrrLOYMmUKzzzzDLquH3b+JJgRQoijwIQJE1i2bBmzZs1CURQURWHOnDkBrSJz5swhMjKSBQsW0LNnT2w2GxdddBHFxcW89957dOrUiaioKO68807cbrd/2zXdyPbu3cv48eOJiooiJCSEvn37snDhQvbs2cPo0aMBiIqKQlEUJkyYAMCiRYs44YQTiIyMJCYmhrPPPpudO3f6t7lnzx4UReGzzz5j1KhRWK1Wjj32WLZt28aff/7J0KFDCQ0N5YwzziAjIyPgOJx33nk8+uijxMXFER4ezi233ILD4Wi8gy+EEEewiRMnsnLlSubOnctff/3FxRdfzOmnn8727dsB7z3CYrEErGO1WklOTmbv3r2HvX8JZoQQ4igwa9YsRowYwY033khqaiqpqakkJSVVSldcXMyLL77I3LlzWbRoEUuXLuX8889n4cKFLFy4kA8++IA33niDefPm+dep6UZ2++23Y7fbWb58OZs2beKZZ54hNDSUpKQkvvjiCwC2bt1Kamoqs2bNArzdGiZNmsSaNWtYvHgxqqpy/vnn4/F4AvI7depUHnroIdatW4fBYOCKK67ggQceYNasWfz666/s2LGDRx55JGCdxYsXs3nzZpYuXconn3zCl19+yaOPPtqgx1sIIY4G+/btY/bs2Xz++eeMGjWKrl27ct9993HCCScwe/ZsAMaNG8eXX37J4sWL8Xg8bNu2jeeffx6A1NTUw8+ELoQQ4qhw0kkn6XfddZf/9ZIlS3RAz8nJ0XVd12fPnq0D+o4dO/xpbr75Zt1ms+kFBQX+ZePGjdNvvvlmXdd1fe/evbqmaXpKSkrAvsaMGaNPmTJF13Vd79+/vz5t2rSgeaqYh6pkZGTogL5p0yZd13V99+7dOqC//fbb/jSffPKJDuiLFy/2L5s+fbres2dP/+trr71Wj46O1ouKivzLXnvtNT00NFR3u93V5kEIIY52gD5//nz/6wULFuiAHhISEvBnMBj0Sy65RNd1Xfd4PPoDDzygWywWXdM0PSoqSp82bZoO6KtWrTrsPBkOPxwSQghxpLDZbHTt2tX/Oj4+nk6dOhEaGhqwLD09HYBNmzbhdrvp0aNHwHbsdjsxMTEA3Hnnndx66638+OOPjB07lgsvvJABAwZUm4/t27fzyCOPsHr1ajIzM/0tMvv27aNfv37+dOW3Ex8fD0D//v2D5tVn4MCB2Gw2/+sRI0ZQWFjI/v376dixY7X5EkIIcUhhYSGaprF27Vo0TQt4z3ffUBSFZ555hqeeeoq0tDTi4uJYvHgxAF26dDnsPEgwI4QQws9oNAa8VhQl6DJfcFGbG9kNN9zAuHHj+O677/jxxx+ZPn06zz//PHfccUeV+Rg/fjwdO3bkrbfeIiEhAY/HQ79+/SqNbSmfN99EBhWXVeyaJoQQomEMHjwYt9tNeno6o0aNqjatpmkkJiYC8MknnzBixAji4uIOOw8SzAghxFHCZDIFDNxvCLW9kSUlJXHLLbdwyy23MGXKFN566y3uuOMOTCYTQEC+srKy2Lp1K2+99ZZ/m7/99luD5Xnjxo2UlJRgtVoBWLVqlX8MjxBCiECFhYXs2LHD/3r37t1s2LCB6OhoevTowZVXXsk111zD888/z+DBg8nIyGDx4sUMGDCAs846i8zMTObNm8fJJ59MaWmpf4zNsmXLGiR/MgGAEEIcJTp16sTq1avZs2dPQNetw1H+Rvbll1+ye/du/vjjD6ZPn853330HwN13380PP/zA7t27WbduHUuWLKF3794AdOzYEUVRWLBgARkZGRQWFhIVFUVMTAxvvvkmO3bs4JdffmHSpEmHnVcfh8PB9ddfz7///svChQuZOnUqEydORFXlliiEEBWtWbOGwYMHM3jwYAAmTZrE4MGD/ZOrzJ49m2uuuYZ7772Xnj17ct555/Hnn3/SoUMH/zbee+89hg4dyvHHH88///zD0qVLGTZsWIPkT1pmhBDiKHHfffdx7bXX0qdPH0pKSvwzzRyu2bNn88QTT3DvvfeSkpJCbGwsxx13HGeffTbgbXW5/fbbSU5OJjw8nNNPP50ZM2YAkJiYyKOPPsrkyZO57rrruOaaa5gzZw5z587lzjvvpF+/fvTs2ZMXX3yRk08+uUHyO2bMGLp3786JJ56I3W7n8ssvZ9q0aQ2ybSGEONKcfPLJ1T4Pxmg08uijj1Y5K2RsbCwrV65srOyh6NXlTgghhDiCTJgwgdzcXL766qvmzooQQogGIG3qQgghhBBCiFZJghkhhBBCCCFEqyTdzIQQQgghhBCtkrTMCCGEEEIIIVolCWaEEEIIIYQQrZIEM0IIIYQQQohWSYIZIYQQQgghRKskwYwQQgghhBCiVZJgRgghhBBCCNEqSTAjhBBCCCGEaJUkmBFCCCGEEEK0ShLMCCGEEEIIIVql/wcRUQMQMB/fVgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure(figsize=(10, 2))\n",
    "ax = fig.gca()\n",
    "\n",
    "t = data['timestamp'].values\n",
    "\n",
    "data.plot(x='timestamp', y='value', ax=ax)\n",
    "plt.plot(t[100:], context['fy_hat'], label='fy')\n",
    "plt.plot(t[:-100], context['ry_hat'], label='ry')\n",
    "\n",
    "plt.plot(t[:-1], context['errors'], label='errors')\n",
    "plt.legend(ncols=4)\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a15a1d20",
   "metadata": {},
   "source": [
    "### find anomalies\n",
    "this primitive extracts anomalies from sequences of errors following the approach explained in the [related paper](https://arxiv.org/pdf/1802.04431.pdf).\n",
    "\n",
    "* **input**: \n",
    "    - `errors` array of errors.\n",
    "    - `target_index` array of indices of errors.\n",
    "* **output**: `y` array containing start-index, end-index, score for each anomalous sequence that was found."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ac7ad495",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['index', 'X_index', 'y_index', 'ry_hat', 'y_hat', 'fy_hat', 'errors', 'X', 'y'])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "step = 7\n",
    "\n",
    "context = pipeline.fit(**context, output_=step, start_=step)\n",
    "context.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1aebdbc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>severity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.400998e+09</td>\n",
       "      <td>1.405685e+09</td>\n",
       "      <td>0.188572</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          start           end  severity\n",
       "0  1.400998e+09  1.405685e+09  0.188572"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(context['y'], columns=['start', 'end', 'severity'])"
   ]
  }
 ],
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